Prediction techniques for interpolation in video coding

ABSTRACT

This disclosure describes filtering techniques applied by an encoder and a decoder during the prediction stage of a video encoding and/or decoding process. The filtering techniques may enhance the accuracy of predictive data used during fractional interpolation, and may improve predictive data of integer blocks of pixels. There are several aspects to this disclosure, including a useful twelve-pixel filter support that may be used for interpolation, techniques that use coefficient symmetry and pixel symmetry to reduce the amount of data needed to be sent between an encoder and a decoder to configure the filter support for interpolation, and techniques for filtering data at integer pixel locations in a manner that is similar to sub-pixel interpolation. Other aspects of this disclosure concern techniques for encoding information in the bitstream to convey the type of filter used, and possibly the filter coefficients used. Predictive coding of filter coefficients is also described.

This application claims the benefit of U.S. Provisional Application61/044,020 filed on Apr. 10, 2008, U.S. Provisional Application61/044,023 filed on Apr. 10, 2008, U.S. Provisional Application61/044,240 filed on Apr. 11, 2008, and U.S. Provisional Application No.61/057,373 filed on May 30, 2008, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

This disclosure relates to digital video encoding and decoding and, moreparticularly, filtering techniques applied to generate predictive dataused in the video encoding and decoding.

BACKGROUND

Digital video capabilities can be incorporated into a wide range ofdevices, including digital televisions, digital direct broadcastsystems, wireless broadcast systems, personal digital assistants (PDAs),laptop or desktop computers, digital cameras, digital recording devices,video gaming devices, video game consoles, cellular or satellite radiotelephones, and the like. Digital video devices implement videocompression techniques, such as those described in standards defined byMPEG-2, MPEG-4, or ITU-T H.264/MPEG-4, Part 10, Advanced Video Coding(AVC), to transmit and receive digital video information moreefficiently. Video compression techniques may perform spatial predictionand/or temporal prediction to reduce or remove redundancy inherent invideo sequences.

Block based inter-coding is a very useful coding technique that relieson temporal prediction to reduce or remove temporal redundancy betweenvideo blocks of successive coded units of a video sequence. The codedunits may comprise video frames, slices of video frames, groups ofpictures, or another defined unit of encoded video blocks. Forinter-coding, the video encoder performs motion estimation and motioncompensation to track the movement of corresponding video blocks of twoor more adjacent coded units. Motion estimation generates motionvectors, which indicate the displacement of video blocks relative tocorresponding prediction video blocks in one or more reference frames orother coded units. Motion compensation uses the motion vectors togenerate prediction video blocks from the one or more reference framesor other coded units. After motion compensation, residual video blocksare formed by subtracting prediction video blocks from the originalvideo blocks being coded.

The video encoder may also apply transform, quantization and entropycoding processes to further reduce the bit rate associated withcommunication of residual blocks. Transform techniques may comprisediscrete cosine transforms (DCTs) or conceptually similar processes.Alternatively, wavelet transforms, integer transforms, or other types oftransforms may be used. In a DCT process, as an example, a set of pixelvalues are converted into transform coefficients, which may representthe energy of the pixel values in the frequency domain. Quantization isapplied to the transform coefficients, and generally involves a processthat reduces the number of bits associated with any given transformcoefficient. Entropy coding comprises one or more processes thatcollectively compress a sequence of coding modes, motion information,coded block patterns, and quantized transform coefficients. Examples ofentropy coding include but are not limited to content adaptive variablelength coding (CAVLC) and context adaptive binary arithmetic coding(CABAC).

A coded video block may be represented by prediction information thatcan be used to create or identify a predictive block, and a residualblock of data indicative of differences between the block being codedand the predictive block. The prediction information may comprise theone or more motion vectors that are used to identify the predictiveblock of data. Given the motion vectors, the decoder is able toreconstruct the predictive blocks that were used to code the residual.Thus, given a set of residual blocks and a set of motion vectors (andpossibly some additional syntax), the decoder can reconstruct a videoframe that was originally encoded. Inter-coding based on motionestimation and motion compensation can achieve very good compressionbecause successive video frames or other types of coded units are oftenvery similar. An encoded video sequence may comprise blocks of residualdata, motion vectors, and possibly other types of syntax.

Interpolation techniques have been developed in order to improve thelevel of compression that can be achieved in inter-coding. In this case,the predictive data generated during motion compensation, which is usedto code a video block, may be interpolated from the pixels of videoblocks of the video frame or other coded unit used in motion estimation.Interpolation is often performed to generate predictive half pixel(half-pel) values and predictive quarter pixel (quarter-pel) values. Thehalf- and quarter-pel values are associated with sub-pixel locations.Fractional motion vectors may be used to identify video blocks at thesub-pixel resolution in order to capture fractional movement in a videosequence, and thereby provide predictive blocks that are more similar tothe video blocks being coded than the integer video blocks.

SUMMARY

In general, this disclosure describes filtering techniques applied by anencoder and a decoder during the prediction stage of a video encodingand/or decoding process. The described filtering techniques may enhancethe accuracy of predictive data used during fractional interpolation,and in some cases, may improve predictive data of integer blocks ofpixels. There are several aspects to this disclosure, including a usefultwelve-pixel filter support that may be used for interpolation,techniques that use coefficient symmetry and pixel symmetry to reducethe amount of data needed to be sent between an encoder and a decoder toconfigure the filter support for interpolation, and techniques forfiltering data at integer pixel locations in a manner that is similar tosub-pixel interpolation. Other aspects of this disclosure concerntechniques for encoding information in the bitstream to convey the typeof filter used, and possibly the filter coefficients used. Predictiveencoding techniques for filter coefficients are also described. Theseand other aspects of this disclosure will become apparent from thedescription below.

In one example, this disclosure describes a method comprisingidentifying a set of filter coefficients for interpolation of predictivedata in video encoding, generating residual values associated with theset of filter coefficients based on predictive coding of the set offilter coefficients relative to filter coefficients associated with afixed interpolation filter, applying quantization to the residualvalues, and entropy coding and outputting the quantized residual valuesas part of an encoded bitstream.

In another example, this disclosure describes a method comprisingreceiving residual values associated with a set of filter coefficients,generating the set of filter coefficients using predictive decodingbased on the set of residual values and filter coefficients associatedwith a fixed interpolation filter, and applying the set of filtercoefficients to interpolate predictive data used for predictive decodingof video blocks.

In another example, this disclosure describes an apparatus comprising avideo encoder that identifies a set of filter coefficients forinterpolation of predictive data in video encoding, generates residualvalues associated with the set of filter coefficients based onpredictive coding of the set of filter coefficients relative to filtercoefficients associated with a fixed interpolation filter, appliesquantization to the residual values, and entropy codes and outputs thequantized residual values as part of an encoded bitstream.

In another example, this disclosure describes an apparatus comprising avideo decoder that receives residual values associated with a set offilter coefficients, generates the set of filter coefficients usingpredictive decoding based on the set of residual values and filtercoefficients associated with a fixed interpolation filter, and appliesthe set of filter coefficients to interpolate predictive data used forpredictive decoding of video blocks.

In another example, this disclosure describes a device comprising meansfor identifying a set of filter coefficients for interpolation ofpredictive data in video encoding, means for generating residual valuesassociated with the set of filter coefficients based on predictivecoding of the set of filter coefficients relative to filter coefficientsassociated with a fixed interpolation filter, means for applyingquantization to the residual values, and means for entropy coding andoutputting the quantized residual values as part of an encodedbitstream.

In another example, this disclosure describes a device comprising meansfor receiving residual values associated with a set of filtercoefficients, means for generating the set of filter coefficients usingpredictive decoding based on the set of residual values and filtercoefficients associated with a fixed interpolation filter, and means forapplying the set of filter coefficients to interpolate predictive dataused for predictive decoding of video blocks.

The techniques described in this disclosure may be implemented inhardware, software, firmware, or any combination thereof. If implementedin software, the software may be executed in one or more processors,such as a microprocessor, application specific integrated circuit(ASIC), field programmable gate array (FPGA), or digital signalprocessor (DSP). The software that executes the techniques may beinitially stored in a computer-readable medium and loaded and executedin the processor.

Accordingly, this disclosure also contemplates a computer-readablestorage medium comprising instructions that when executed by a processorcause the processor to identify a set of filter coefficients forinterpolation of predictive data in video encoding, generate residualvalues associated with the set of filter coefficients based onpredictive coding of the set of filter coefficients relative to filtercoefficients associated with a fixed interpolation filter, applyquantization to the residual values, and entropy code and output thequantized residual values as part of an encoded bitstream.

In another example, this disclosure describes a computer-readablestorage medium comprising instructions that when executed by a processorcause the processor to upon receiving residual values associated with aset of filter coefficients, generate the set of filter coefficientsusing predictive decoding based on the set of residual values and filtercoefficients associated with a fixed interpolation filter, and apply theset of filter coefficients to interpolate predictive data used forpredictive decoding of video blocks.

The details of one or more aspects of the disclosure are set forth inthe accompanying drawings and the description below. Other features,objects, and advantages of the techniques described in this disclosurewill be apparent from the description and drawings, and from the claims.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a block diagram illustrating one exemplary video encoding anddecoding system that may implement techniques of this disclosure.

FIG. 2 is a block diagram illustrating an example of a video encoderthat may perform filtering techniques consistent with this disclosure.

FIG. 3 is a conceptual diagram illustrating integer-pixel positionsassociated with prediction data, and sub-pixel positions associated withinterpolated prediction data.

FIG. 4 is a conceptual diagram illustrating a 12 pixel filter supportwith respect to nine sub-pixel locations.

FIG. 5 is a conceptual diagram illustrating a horizontal 6 pixel filtersupport with respect three horizontal sub-pixel locations, and avertical 6 pixel filter support with respect three vertical sub-pixellocations.

FIG. 6 is a conceptual diagram illustrating a five pixel-by-five pixelfilter support for filtering an integer pixel location.

FIG. 7 is a conceptual diagram illustrating four integer pixel positionsand fifteen sub-pixel positions with shading to group pixel positionsthat may use pixel symmetry for filter coefficients consistent with thisdisclosure.

FIG. 8 is a conceptual diagram illustrating six horizontal linear pixelsupport positions relative to a sub-pixel, with shading that showscoefficient symmetry.

FIG. 9 is a conceptual diagram illustrating six horizontal linear pixelsupport positions relative to a sub-pixel, with shading that shows alack of any coefficient symmetry.

FIG. 10 is a conceptual diagram illustrating six vertical linear pixelsupport positions relative to a sub-pixel, with shading that showscoefficient symmetry.

FIG. 11 is a conceptual diagram illustrating six vertical linear pixelsupport positions relative to a sub-pixel, with shading that shows alack of any coefficient symmetry.

FIG. 12 is a conceptual diagram illustrating twelve two-dimensionalpixel support positions relative to a sub-pixel, with shading that showsa lack of any coefficient symmetry.

FIG. 13 is a conceptual diagram illustrating twelve two-dimensionalpixel support positions relative to a sub-pixel, with shading that showscoefficient symmetry.

FIG. 14 is a conceptual diagram illustrating twelve two-dimensionalpixel support positions relative to a sub-pixel, with shading that showscoefficient symmetry.

FIG. 15 is a conceptual diagram illustrating twelve two-dimensionalpixel support positions relative to a sub-pixel, with shading that showscoefficient symmetry.

FIG. 16 is a block diagram illustrating an example of a video decoder,which may decode a video sequence that is encoded in the mannerdescribed herein.

FIG. 17 is a flow diagram illustrating example operation of a videoencoder that utilizes a twelve pixel filter support consistent with thisdisclosure.

FIG. 18 is a flow diagram illustrating example operation of a videodecoder that utilizes a twelve pixel filter support consistent with thisdisclosure.

FIG. 19 is a flow diagram illustrating example operation of a videoencoder that utilizes coefficient symmetry and pixel symmetry consistentwith this disclosure.

FIG. 20 is a flow diagram illustrating example operation of a videodecoder that utilizes coefficient symmetry and pixel symmetry consistentwith this disclosure.

FIG. 21 is a flow diagram illustrating example operation of a videoencoder that utilizes filtering of integer pixel locations to generateadjusted integer pixel values consistent with this disclosure.

FIG. 22 is a flow diagram illustrating example operation of a videodecoder that utilizes filtering of integer pixel locations to generateadjusted integer pixel values consistent with this disclosure.

FIG. 23 is a flow diagram illustrating a technique for rate-distortiondefined interpolation for video coding based on a fixed filter or anadaptive filter.

FIG. 24 is a flow diagram illustrating a technique for encoding filtercoefficients using predictive coding.

FIG. 25 is another flow diagram illustrating a technique for encodingfilter coefficients using predictive coding.

FIG. 26 is a flow diagram illustrating a technique for decoding filtercoefficients using predictive coding.

FIGS. 27 and 28 are conceptual graphs illustrating filter coefficientsthat can be predictively coded.

FIG. 29 is an illustrative example of an array of integer-pixel filtercoefficients for which prediction techniques may be used for encoding.

DETAILED DESCRIPTION

This disclosure describes filtering techniques applied by an encoder anda decoder during the prediction stage of a video encoding and/ordecoding process. The described filtering techniques may enhance theaccuracy of predictive data used during fractional interpolation, and insome cases, may improve predictive data of integer blocks of pixels.There are several aspects to this disclosure, including a usefultwelve-pixel filter support that may be used for interpolation,techniques that use coefficient symmetry and pixel symmetry to reducethe amount of data needed to be sent between an encoder and a decoder toconfigure the filter support for interpolation, and techniques forfiltering data at integer pixel locations in a manner that is similar tosub-pixel interpolation. These and other techniques are described indetail below.

FIG. 1 is a block diagram illustrating one exemplary video encoding anddecoding system 10 that may be used to implement one or more of thetechniques of this disclosure. As shown in FIG. 1, system 10 includes asource device 12 that transmits encoded video to a destination device 16via a communication channel 15. Source device 12 and destination device16 may comprise any of a wide range of devices. In some cases, sourcedevice 12 and destination device 16 comprise wireless communicationdevices, such as wireless handsets, so-called cellular or satelliteradiotelephones, or any wireless devices that can communicate videoinformation over a communication channel 15, in which case communicationchannel 15 is wireless. The techniques of this disclosure, however,which concern filtering and the generation of predictive data duringpredictive coding, are not necessarily limited to wireless applicationsor settings. The techniques may also be useful in a wide rage of othersettings and devices, including devices that communicate via physicalwires, optical fibers or other physical or wireless media. In addition,the encoding or decoding techniques may also be applied in a stand alonedevice that does not necessarily communicate with any other device.

In the example of FIG. 1, source device 12 may include a video source20, video encoder 22, modulator/demodulator (modem) 23 and transmitter24. Destination device 16 may include a receiver 26, modem 27, videodecoder 28, and display device 30. In accordance with this disclosure,video encoder 22 of source device 12 may be configured to apply one ormore of the techniques of this disclosure as part of a video encodingprocess. Similarly, video decoder 28 of destination device 16 may beconfigured to apply one or more of the techniques of this disclosure aspart of a video decoding process.

Again, the illustrated system 10 of FIG. 1 is merely exemplary. Thevarious techniques of this disclosure may be performed by any encodingdevice that supports block-based predictive encoding, or by any decodingdevice that supports block-based predictive decoding. Source device 12and destination device 16 are merely examples of such coding devices inwhich source device 12 generates coded video data for transmission todestination device 16. In some cases, devices 12, 16 may operate in asubstantially symmetrical manner such that, each of devices 12, 16include video encoding and decoding components. Hence, system 10 maysupport one-way or two-way video transmission between video devices 12,16, e.g., for video streaming, video playback, video broadcasting, orvideo telephony.

Video source 20 of source device 12 may include a video capture device,such as a video camera, a video archive containing previously capturedvideo, or a video feed from a video content provider. As a furtheralternative, video source 20 may generate computer graphics-based dataas the source video, or a combination of live video, archived video, andcomputer-generated video. In some cases, if video source 20 is a videocamera, source device 12 and destination device 16 may form so-calledcamera phones or video phones. In each case, the captured, pre-capturedor computer-generated video may be encoded by video encoder 22. Theencoded video information may then be modulated by modem 23 according toa communication standard, e.g., such as code division multiple access(CDMA) or another communication standard, and transmitted to destinationdevice 16 via transmitter 24 and communication channel 15. Modem 23 mayinclude various mixers, filters, amplifiers or other components designedfor signal modulation. Transmitter 24 may include circuits designed fortransmitting data, including amplifiers, filters, and one or moreantennas.

Receiver 26 of destination device 16 receives information overcommunication channel 15, and modem 27 demodulates the information. Liketransmitter 24, receiver 26 may include circuits designed for receivingdata, including amplifiers, filters, and one or more antennas. In someinstances, transmitter 24 and/or receiver 26 may be incorporated withina single transceiver component that include both receive and transmitcircuitry. Modem 27 may include various mixers, filters, amplifiers orother components designed for signal demodulation. In some instances,modems 23 and 27 may include components for performing both modulationand demodulation.

Again, the video encoding process performed by video encoder 22 mayimplement one or more of the techniques described herein during motioncompensation. The video decoding process performed by video decoder 28may also perform such techniques during its motion compensation stage ofthe decoding process. The term “coder” is used herein to refer to aspecialized computer device or apparatus that performs video encoding orvideo decoding. The term “coder” generally refers to any video encoder,video decoder, or combined encoder/decoder (codec). The term “coding”refers to encoding or decoding. Display device 30 displays the decodedvideo data to a user, and may comprise any of a variety of displaydevices such as a cathode ray tube (CRT), a liquid crystal display(LCD), a plasma display, an organic light emitting diode (OLED) display,or another type of display device.

In the example of FIG. 1, communication channel 15 may comprise anywireless or wired communication medium, such as a radio frequency (RF)spectrum or one or more physical transmission lines, or any combinationof wireless and wired media. Communication channel 15 may form part of apacket-based network, such as a local area network, a wide-area network,or a global network such as the Internet. Communication channel 15generally represents any suitable communication medium, or collection ofdifferent communication media, for transmitting video data from sourcedevice 12 to destination device 16. Communication channel 15 may includerouters, switches, base stations, or any other equipment that may beuseful to facilitate communication from source device 12 to destinationdevice 16.

Video encoder 22 and video decoder 28 may operate according to a videocompression standard, such as the ITU-T H.264 standard, alternativelydescribed as MPEG-4, Part 10, Advanced Video Coding (AVC). Thetechniques of this disclosure, however, are not limited to anyparticular video coding standard. Although not shown in FIG. 1, in someaspects, video encoder 22 and video decoder 28 may each be integratedwith an audio encoder and decoder, and may include appropriate MUX-DEMUXunits, or other hardware and software, to handle encoding of both audioand video in a common data stream or separate data streams. Ifapplicable, MUX-DEMUX units may conform to the ITU H.223 multiplexerprotocol, or other protocols such as the user datagram protocol (UDP).

Video encoder 22 and video decoder 28 each may be implemented as one ormore microprocessors, digital signal processors (DSPs), applicationspecific integrated circuits (ASICs), field programmable gate arrays(FPGAs), discrete logic, software, hardware, firmware or anycombinations thereof. Each of video encoder 22 and video decoder 28 maybe included in one or more encoders or decoders, either of which may beintegrated as part of a combined codec that provides encoding anddecoding capabilities in a respective mobile device, subscriber device,broadcast device, server, or the like.

A video sequence typically includes a series of video frames. Videoencoder 22 operates on video blocks within individual video frames inorder to encode the video data. The video blocks may have fixed orvarying sizes, and may differ in size according to a specified codingstandard. Each video frame includes a series of slices. Each slice mayinclude a series of macroblocks, which may be arranged into sub-blocks.As an example, the ITU-T H.264 standard supports intra prediction invarious block sizes, such as 16 by 16, 8 by 8, or 4 by 4 for lumacomponents, and 8×8 for chroma components, as well as inter predictionin various block sizes, such as 16 by 16, 16 by 8, 8 by 16, 8 by 8, 8 by4, 4 by 8 and 4 by 4 for luma components and corresponding scaled sizesfor chroma components. Video blocks may comprise blocks of pixel data,or blocks of transformation coefficients, e.g., following atransformation process such as discrete cosine transform (DCT) or aconceptually similar transformation process.

Smaller video blocks can provide better resolution, and may be used forlocations of a video frame that include high levels of detail. Ingeneral, macroblocks and the various sub-blocks may be considered to bevideo blocks. In addition, a slice may be considered to be a series ofvideo blocks, such as macroblocks and/or sub-blocks. Each slice may bean independently decodable unit of a video frame. Alternatively, framesthemselves may be decodable units, or other portions of a frame may bedefined as decodable units. The term “coded unit” refers to anyindependently decodable unit of a video frame such as an entire frame, aslice of a frame, or another independently decodable unit definedaccording to the coding techniques used.

To encode the video blocks, video encoder 22 performs intra- orinter-prediction to generate a prediction block. Video encoder 22subtracts the prediction blocks from the original video blocks to beencoded to generate residual blocks. Thus, the residual blocks areindicative of differences between the blocks being coded and theprediction blocks. Video encoder 22 may perform a transform on theresidual blocks to generate blocks of transform coefficients. Followingintra- or inter-based predictive coding and transformation techniques,video encoder 22 performs quantization. Quantization generally refers toa process in which coefficients are quantized to possibly reduce theamount of data used to represent the coefficients. Followingquantization, entropy coding may be performed according to an entropycoding methodology, such as context adaptive variable length coding(CAVLC) or context adaptive binary arithmetic coding (CABAC). Moredetails of each step of the encoding process performed by video encoder22 will be described in more detail below in FIG. 2.

In destination device 16, video decoder 28 receives the encoded videodata. Video decoder 28 entropy decodes the received video data accordingto an entropy coding methodology, such as CAVLC or CABAC, to obtain thequantized coefficients. Video decoder 28 applies inverse quantization(de-quantization) and inverse transform functions to reconstruct theresidual block in the pixel domain. Video decoder 28 also generates aprediction block based on control information or syntax information(e.g., coding mode, motion vectors, syntax that defines filtercoefficients and the like) included in the encoded video data. Videodecoder 28 sums the prediction block with the reconstructed residualblock to produce a reconstructed video block for display. More detailsof each step of the encoding process performed by video encoder 22 willbe described in more detail below in FIG. 16.

According to the techniques of this disclosure, video encoder 22 andvideo decoder 28 may use the one or more interpolation filteringtechniques during motion compensation. In particular, in accordance withone aspect of this disclosure, video encoder 22 and/or video decoder 28may obtain a block of pixels, wherein the block of pixels includesinteger pixel values corresponding to integer pixel positions within theblock of pixels, compute sub-pixel values for sub-pixel positionsassociated with the block of pixels based on the integer pixel values,wherein computing the sub-pixel values comprises applying aninterpolation filter that defines a two-dimensional array of filtersupport positions corresponding to a set of twelve or more integer pixelpositions that surround the sub-pixel positions in a radial shape, andgenerate a prediction block based on at least some of the sub-pixelvalues. An example of the two-dimensional array of filter supportpositions corresponding to a set of twelve or more integer pixelpositions is explained in greater detail below.

In accordance with another aspect of this disclosure, video encoder 22and/or video decoder 28 may utilize aspects of symmetry in order toreduce the amount of data that needs to be communicated between sourcedevice 12 and destination device 16 for communication of filtercoefficients used in interpolation. Video encoder 22 may determine eightsets of filter coefficients for fifteen different sub-pixel locations,wherein the eight sets of filter coefficients are generated based oncoefficient symmetry and pixel symmetry among fifteen sub-pixellocations, and output the eight sets of filter coefficients to anotherdevice as part of an encoded bitstream. In this way, the eight sets,along with aspects of pixel symmetry and coefficients symmetry maydefine all of the filter coefficients for all fifteen half-pel andquarter-pel pixel positions. Moreover, pixel symmetry may exist betweendifferent ones of the fifteen sub-pixel locations in a verticaldimension and in a horizontal dimension, but pixel symmetry may notexist in a diagonal dimension for at least some of the fifteen setssub-pixel locations. This lack of pixel symmetry in the diagonaldimension for at least some of the fifteen locations may improveinterpolations and video quality in the video encoding and decoding.

Video decoder 28 of destination device 16 may receive the eight sets offilter coefficients as part of an encoded video bitstream, the generatefifteen sets of filter coefficients corresponding to fifteen differentsub-pixel locations based on the eight sets of filter coefficients,generate interpolated predictive data for video decoding based on one ofthe fifteen sets of filter coefficients, wherein the interpolatedpredictive data corresponds to one of the fifteen different sub-pixellocations, and decode one or more video blocks base on the interpolatedpredictive data.

In accordance with another aspect of this disclosure, video encoder 22and/or video decoder 28 may utilize interpolation-like filtering withrespect to integer pixel positions in order to generate adjusted integerpixel values. Such interpolation-like filtering may improve compressionspecifically during illumination changes, scene fade-ins or fade-outs,may remove noise and facilitate image frame sharpening, and may helpimprove encoding of fine object movement between successive video framesparticularly when symmetry is not imposed on filter coefficients.

The interpolation-like filtering techniques of video encoder 22 and/orvideo decoder 28 may include obtaining blocks of pixels, wherein theblocks of pixels includes integer pixel values corresponding to integerpixel positions within the blocks of pixels, filtering the integer pixelvalues based on other integer pixel values within the block of pixels togenerate adjusted integer pixel values, wherein the adjusted integerpixel values correspond to the integer pixel positions, and generating aprediction block based on the adjusted integer pixel values.

In accordance with another aspect of this disclosure video encoder 22may generate first interpolated predictive data for encoding of videodata based on a first interpolation filter, generate second interpolatedpredictive data for video encoding of the video data based on a secondinterpolation filter, select between the first interpolated predictivedata and the second interpolated predictive data based on arate-distortion analysis, encode the video data based on the selection,and encode syntax to indicate the selection. The first interpolationfilter may comprise a fixed interpolation filter, and the secondinterpolation filter may comprise an adaptive interpolation filter, butthis disclosure is not necessarily limited to these examples.

Furthermore, additional interpolation filters may also be applied togenerate additional interpolated predictive data, which may also beconsidered in the rate distortion analysis. In other words, thetechniques of this disclosure are not limited to generating only firstand second interpolated predictive data based on two interpolationfilters, but could be applied to generate any plurality of interpolatedpredictive data based on any number of interpolation filters.Importantly, a rate-distortion analysis interpolated predictive data isused to identify which filter to select.

In one example, a method may comprise generating a plurality ofdifferent versions of predictive data for encoding of video data basedon a plurality of different interpolation filters, selecting among theplurality of different versions of predictive data based on a ratedistortion analysis, encoding the video data based on the selection, andencoding syntax to indicate the selection.

This disclosure also contemplates technique for encoding filtercoefficients. For example, video encoder 22 may identify a set of filtercoefficients for interpolation of predictive data in video encoding,generate residual values associated with the set of filter coefficientsbased on predictive coding of the set of filter coefficients relative tofilter coefficients associated with a fixed interpolation filter, applyquantization to the residual values, and output the quantized residualvalues as part of an encoded bitstream.

Video decoder 28 may receive residual values associated with a set offilter coefficients, generate the set of filter coefficients usingpredictive decoding based on the set of residual values and filtercoefficients associated with a fixed interpolation filter, and apply theset of filter coefficients to interpolate predictive data used forpredictive decoding of video blocks.

FIG. 2 is a block diagram illustrating an example of a video encoder 50that may perform filtering techniques consistent with this disclosure.Video encoder 50 is one example of a specialized video computer deviceor apparatus referred to herein as a “coder.” Video encoder 50 maycorrespond to video encoder 22 of device 20, or a video encoder of adifferent device. Video encoder 50 may perform intra- and inter-codingof blocks within video frames, although intra-coding components are notshown in FIG. 2 for ease of illustration. Intra-coding relies on spatialprediction to reduce or remove spatial redundancy in video within agiven video frame. Inter-coding relies on temporal prediction to reduceor remove temporal redundancy in video within adjacent frames of a videosequence. Intra-mode (I-mode) may refer to the spatial based compressionmode, and Inter-modes such as a prediction (P-mode) or a bi-directional(B-mode) may refer to the temporal based compression modes. Thetechniques of this disclosure apply during inter-coding, and therefore,intra-coding units such as spatial prediction unit are not illustratedin FIG. 2 for simplicity and ease of illustration.

As shown in FIG. 2, video encoder 50 receives a video block within avideo frame to be encoded. In the example of FIG. 2, video encoder 50includes a prediction unit 32, memory 34, an adder 48, a transform unit38, a quantization unit 40, and an entropy coding unit 46. For videoblock reconstruction, video encoder 50 also includes an inversequantization unit 42, an inverse transform unit 44, and an adder 51. Adeblocking filter (not shown) may also be included to filter blockboundaries to remove blockiness artifacts from reconstructed video. Ifdesired, the deblocking filter would typically filter the output ofadder 51.

Prediction unit 32 may include a motion estimation (ME) unit 35, and amotion compensation (MC) unit 37. Filter 37 may be included inprediction unit 32 and may be invoked by one or both of ME unit 35 andMC unit 37 to perform interpolation or interpolation-like filtering aspart of motion estimation and/or motion compensation, according to thisdisclosure. Filter 37 may actually represent a plurality of differentfilters to facilitate numerous different types of interpolation andinterpolation-type filtering as described herein. Thus, prediction unit32 may include a plurality of interpolation or interpolation-likefilters. During the encoding process, video encoder 50 receives a videoblock to be coded (labeled “VIDEO BLOCK” in FIG. 2), and prediction unit32 performs inter-prediction coding to generate a prediction block(labeled “PRED. BLOCK” in FIG. 2). Specifically, ME unit 35 may performmotion estimation to identify the prediction block in memory 34, and MCunit 37 may perform motion compensation to generate the predictionblock.

Motion estimation is typically considered the process of generatingmotion vectors, which estimate motion for video blocks. A motion vector,for example, may indicate the displacement of a prediction block withina prediction or reference frame (or other coded unit, e.g., slice)relative to the block to be coded within the current frame (or othercoded unit). The reference frame (or portion of the frame) may betemporally located prior to or after the video frame (or portion of thevideo frame) to which the current video block belongs. Motioncompensation is typically considered the process of fetching orgenerating the prediction block from memory 34, or possiblyinterpolating or otherwise generating filtered predictive data based onthe motion vector determined by motion estimation.

ME unit 35 selects the appropriate motion vector for the video block tobe coded by comparing the video block to video blocks of one or morereference frames (e.g., a previous and/or subsequent frame). ME unit 35may perform motion estimation with fractional pixel precision, sometimesreferred to as fractional pixel, fractional pel, or sub-pixel motionestimation. As such, the terms fractional pixel, fractional pel, andsub-pixel motion estimation may be used interchangeably. In fractionalpixel motion estimation, ME unit 35 may select a motion vector thatindicates displacement to a location other than an integer pixellocation. In this manner, fractional pixel motion estimation allowsprediction unit 32 to track motion with higher precision thaninteger-pixel (or full-pixel) locations, thus generate a more accurateprediction block. Fractional pixel motion estimation may have half-pixelprecision, quarter-pixel precision, eighth-pixel precision or any finerprecision. ME unit 35 may invoke filter(s) 39 for any necessaryinterpolations during the motion estimation process.

To perform fractional pixel motion compensation, MC unit 37 may performinterpolation (sometimes referred to as interpolation filtering) inorder to generate data at sub-pixel resolution (referred to herein assub-pixel or fractional pixel values). MC unit 37 may invoke filter(s)39 for this interpolation. Prediction unit 32 may perform theinterpolation (or interpolation-like filtering of integer pixels) usingthe techniques described herein.

Once the motion vector for the video block to be coded is selected by MEunit 35, MC unit 37 generates the prediction video block associated withthat motion vector. MC unit 37 may fetch the prediction block frommemory 34 based on the motion vector determined by MC unit 35. In thecase of a motion vector with fractional pixel precision, MC unit 37filters data from memory 34 to interpolate such data to sub-pixelresolution, e.g., invoking filter(s) 39 for this process. In some cases,the interpolation filtering technique or mode that was used to generatethe sub-pixel prediction data may be indicated as one or moreinterpolation syntax elements to entropy coding unit 46 for inclusion inthe coded bitstream. Indeed, some aspects of this disclosure concern theuse of pixel symmetry and coefficient symmetry to reduce the amount ofsyntax that needs to be conveyed.

Once prediction unit 32 has generated the prediction block, videoencoder 50 forms a residual video block (labeled “RESID. BLOCK” in FIG.2) by subtracting the prediction block from the original video blockbeing coded. Adder 48 represents the component or components thatperform this subtraction operation. Transform unit 38 applies atransform, such as a discrete cosine transform (DCT) or a conceptuallysimilar transform, to the residual block, producing a video blockcomprising residual transform block coefficients. Transform unit 38, forexample, may perform other transforms, such as those defined by theH.264 standard, which are conceptually similar to DCT. Wavelettransforms, integer transforms, sub-band transforms or other types oftransforms could also be used. In any case, transform unit 38 appliesthe transform to the residual block, producing a block of residualtransform coefficients. The transform may convert the residualinformation from a pixel domain to a frequency domain.

Quantization unit 40 quantizes the residual transform coefficients tofurther reduce bit rate. The quantization process may reduce the bitdepth associated with some or all of the coefficients. Followingquantization, entropy coding unit 46 entropy codes the quantizedtransform coefficients. For example, entropy coding unit 46 may performCAVLC, CABAC, or another entropy coding methodology.

Entropy coding unit 46 may also code one or more prediction syntaxelements obtained from prediction unit 32 or other component of videoencoder 50. The one or more prediction syntax elements may include acoding mode, one or more motion vectors, an interpolation technique thatwas use to generate the sub-pixel data, a set or subset of filtercoefficients, or other information associated with the generation of theprediction block. Coefficient prediction and quantization unit 41 maypredictively encode and quantize the prediction syntax, such as filtercoefficients, according to some aspects of this disclosure. Followingthe entropy coding by entropy coding unit 46, the encoded video andsyntax elements may be transmitted to another device or archived forlater transmission or retrieval.

Inverse quantization unit 42 and inverse transform unit 44 apply inversequantization and inverse transformation, respectively, to reconstructthe residual block in the pixel domain, e.g., for later use as areference block. The reconstructed residual block (labeled “RECON.RESID. BLOCK” in FIG. 2) may represent a reconstructed version of theresidual block provided to transform unit 38. The reconstructed residualblock may differ from the residual block generated by summer 48 due toloss of detail caused by the quantization and inverse quantizationoperations. Summer 51 adds the reconstructed residual block to themotion compensated prediction block produced by prediction unit 32 toproduce a reconstructed video block for storage in memory 34. Thereconstructed video block may be used by prediction unit 32 as areference block that may be used to subsequently code a block in asubsequent video frame or subsequent coded unit.

As described above, prediction unit 32 may perform motion estimationwith fractional pixel (or sub-pixel) precision. When prediction unit 32uses fractional pixel motion estimation, prediction unit 32 may generatedata at sub-pixel resolution (e.g., sub-pixel or fractional pixelvalues) using interpolation operations described in this disclosure. Inother words, the interpolation operations are used to compute values atpositions between the integer pixel positions. Sub-pixel positionslocated half the distance between integer-pixel positions may bereferred to as half-pixel (half-pel) positions, sub-pixel positionslocated half the distance between an integer-pixel position and ahalf-pixel position may be referred to as quarter-pixel (quarter-pel)positions, sub-pixel positions located half the distance between aninteger-pixel position (or half-pixel position) and a quarter-pixelposition are referred to as eighth-pixel (eighth-pel) positions, and thelike.

FIG. 3 is a conceptual diagram illustrating integer pixel (or fullpixel) positions associated with prediction data, and sub-pixel (orfractional-pixel) positions associated with interpolated predictiondata. In the conceptual illustration of FIG. 3, the different boxesrepresent pixel and sub-pixel locations or positions within a frame or ablock of a frame. Capitalized letters (in the boxes with solid lines)represent integer-pixel locations, while small letters (in the boxeswith dotted lines) represent the sub-pixel locations. In particular,pixel locations A1-A6, B1-B6, C1-C6, D1-D6, E1-E6 and F1-F6 represent a6-by-6 array of integer pixel locations within a frame, slice or othercoded unit. Sub-pixel locations “a” through “o” represent fifteensub-pixel locations associated with integer pixel C3, e.g., betweeninteger pixel locations C3, C4, D3 and D4. Similar sub-pixel locationsmay exist for every integer pixel location. The sub-pixel locations “a”through “o” represent every half-pel and quarter-pel pixel locationassociated with integer pixel C3.

Integer-pixel locations may be associated with a physical sensorelement, such as a photodiode when the video data was originallygenerated. The photodiode may measure an intensity of a light source atthe location of the sensor and associate a pixel intensity value withthe integer-pixel location. Again, each integer-pixel location may havean associated set of fifteen sub-pixel locations (or possibly more). Thenumber of sub-pixel locations associated with integer-pixel locationsmay be dependent upon the desired precision. In the example illustratedin FIG. 3, the desired precision is quarter-pixel precision, in whichcase, each of the integer pixel locations corresponds with fifteendifferent sub-pixel positions. More or fewer sub-pixel positions may beassociated with each integer-pixel location based on the desiredprecision. For half-pixel precision, for example, each integer-pixellocation may correspond with three sub-pixel positions. As anotherexample, each of the integer-pixel locations may correspond withsixty-three sub-pixel positions for eighth-pixel precision. Each pixellocation may define one or more pixel values, e.g., one or moreluminance and chrominance values.

Y may represent luminance, and Cb and Cr may represent two differentvalues of chrominance of a three-dimensional YCbCr color space. Eachpixel location may actually define three pixel values for athree-dimensional color space. The techniques of this disclosure,however, may refer to prediction with respect to one dimension forpurposes of simplicity. To the extent that techniques are described withrespect to pixel values in one dimension, similar techniques may beextended to the other dimensions.

In the example of FIG. 3, sub-pixel locations associated with integerpixel “C3” are illustrated for quarter-pixel precision. The fifteensub-pixel positions associated with pixel C3 are labeled as “a,” “b,”“c,” “d,” “e,” “f,” “g,” “h,” “i,” “j,” “k,” “l,” “m,” “n,” and “o.”Most of the other fractional locations associated with otherinteger-pixel locations are not shown for simplicity (other than thoseused to generate one or more of the 15 different fractional locationsassociated with pixel location C3, as described in further detailbelow). Sub-pixel locations “b,” “h” and “j” may be referred to ashalf-pixel locations and sub-pixel locations “a,” “c,” “d,” “e,” “f,”“g,” “i,” “k,” “l,” “m,” and “o” may be referred to as quarter-pixellocations.

Prediction unit 32 of video encoder 40 may determine pixel values forsub-pixel locations “a” through “o” using interpolation filtering by MCunit 37. Consistent with the ITU-T H.264 standard, for example,prediction unit 32 may determine pixel values for half-pixel locationsusing a 6-tap interpolation filter, such as a Wiener filter. In the caseof the H.264 standard, the filter coefficients for the 6-tapinterpolation filter are typically [1, −5, 20, 20, −5, 1], althoughother coefficients may be used. Prediction unit 32 may apply theinterpolation filter first in the horizontal direction and then in thevertical direction, or vice versa. For half-pixel positions “b” and “h,”each tap may correspond to an integer pixel position in the horizontaland vertical direction, respectively. In particular, for half-pixelposition “b,” the taps of the 6-tap filter correspond to C1, C2, C3, C4,C5 and C6. Likewise, for half-pixel position “h,” the taps of the 6-tapfilter correspond to A3, B3, C3, D3, E3 and F3. For example, pixelvalues for sub-pixel positions “b” and “h” may be computed usingequations (1) and (2):b=((C1−5*C2+20*C3+20*C4−5*C5+C6)+16)/32  (1)h=((A3−5*B3+20*C3+20*D3−5*E3+F3)+16)/32  (2)

For half-pixel position “j,” the taps of the 6-tap filter correspondthemselves to interpolated horizontally between positions C1-C6 andD1-D6, or vertically between positions A3-F3 and A4-F4. Half-pixellocation “j” may be computed with a 6-tap filter that uses previouslyinterpolated pixel values of the half-pixel positions, e.g., inaccordance with one of equations (3) or (4):j=((aa−5*bb+20*b+20*hh−5*ii+jj)+16)/32  (3)j=((cc−5*dd+20*h+20*ee−5*ff+gg)+16)/32  (4)where (as illustrated in FIG. 3) aa corresponds to an interpolationbetween A3 and A4, bb corresponds to an interpolation between B3 and B4,b corresponds to an interpolation between C3 and C4, hh corresponds toan interpolation between D3 and D4, ii corresponds to an interpolationbetween E3 and E4 and jj corresponds to an interpolation between F3 andF4. In equation 4, cc corresponds to an interpolation between C1 and D1,dd corresponds to an interpolation between C2 and D2, h corresponds toan interpolation between C3 and D3, ee corresponds to an interpolationbetween C4 and D4, ff corresponds to an interpolation between C5 and D5and gg corresponds to an interpolation between C6 and D6.

Consistent with the H.264 standard, prediction unit 32 may determinepixel values at quarter-pixel locations “a,” “c,” “d,” “e,” “f,” “g,”“i,” “k,” “l,” “m,” “n” and “o” using a bilinear interpolation filterand the pixel values of the surrounding integer- and half-pixellocations. For example, prediction unit 32 may determine a pixel valueassociated with sub-pixel position “a” using pixel values of C3 and “b,”determine a pixel value associated with sub-pixel position “c” usingpixel values of “b” and C4, and the like.

The actual filter that is applied by MC unit 37 to generate interpolateddata at the sub-pixel locations may be subject to a wide variety ofimplementations. As one example, prediction unit 32 may use adaptiveinterpolation filtering (AIF), as described below, to define theinterpolated values. The ITU-T SG16/Q.6/VCEG (Video Coding Expert Group)committee has been exploring coding technologies that offer highercoding efficiency than H.264 and, in particular, AIF. AIF offers largecoding gain over the interpolation filtering used in the H.264 standard,especially on video sequences with high resolution (e.g., 720i/p or1080i/p). In AIF, the interpolation filter for each sub-pixel positionis analytically calculated for each video frame by minimizing theprediction error energy. This helps to address aliasing, quantizationand motion estimation errors, camera noise or other artifact containedin the original and reference video frames. The analytically derivedadaptive filter coefficients for each frame are then predicted,quantized, coded and sent in the video bitstream. Some of the techniquesof this disclosure could work within an AIF scheme, as well as manyother interpolation schemes.

There are many different types of AIF schemes consistent with aspects ofthis disclosure. For example a first scheme is a two-dimensionalnon-separable AIF (NS-AIF), a second is a separable AIF (S-AIF), and athird is an AIF with directional filters (D-AIF). Although each of theseAIF schemes use different interpolation techniques and support, allthree AIF schemes may use similar analytical processes to derive thefilter coefficients, which is explained below using non-separable AIF asan example.

Assume a 6-by-6 two-dimensional non-separable filter has coefficientsh_(i,j) ^(SP) where i,j=0 . . . 5 and SP represents one of the 15sub-pixel positions (“a” through “o”) shown in FIG. 3. Note that 6 ofthe 15 sub-pixel positions, i.e., “a,” “b,” “c,” “d,” “h” and “l,” areone-dimensional (1D) sub-pixel positions, and prediction unit 32 may usea 6-tap interpolation filter to interpolate such data. Sub-pixelpositions “a,” “b,” “c,” “d,” “h” and “l,” are 1D in the sense that theyare located in a horizontal or vertical line between two integer-pixelpositions. Also, assume that the prediction pixels at the integer-pixelpositions (A1 through F6 in FIG. 3) in the reference frame take thepixel values of P_(i,j) where i,j=0 . . . 5. That is, A1 takes the valueof P_(0,0), . . . , A6 takes the value of P_(5,0), . . . , F1 takes thevalue of P_(5,0), . . . , and F6 takes the value of P_(5,5). Then, theinterpolated value p^(SP) at sub-pixel position SP, SPε{a, . . . ,o},may be calculated by prediction unit 32 using the following equation

$\begin{matrix}{p^{SP} = {\sum\limits_{i = 0}^{5}{\sum\limits_{j = 0}^{5}{P_{i,j}{h_{i,j}^{SP}.}}}}} & (5)\end{matrix}$

Let S_(x,y) be the pixel value in the current video frame at position(x, y).{tilde over (x)}=x+└mvx┘−FO, {tilde over (y)}=y+└mvy┘−FO,where (mvx,mvy) is the motion vector, (└mvx┘,└mvy┘) is the integercomponent of the motion vector, and FO is the filter offset. The value({tilde over (x)}, {tilde over (y)}) is the corresponding pixel positionin the reference frames. For example, in the case of 6-tap filter,FO=6/2−1=2. For each sub-pixel position SP, the prediction error energy(e^(SP))² between the actual pixel value in the current frame and theinterpolated value can be accumulated by prediction unit 32 for allpixels that have motion vector precision corresponding to sub-pixelposition SP. The prediction error energy (e^(SP))² may be calculated byprediction unit 32 using the following equation:

$\begin{matrix}{\left( e^{SP} \right)^{2} = {{\sum\limits_{x}{\sum\limits_{y}\left( {S_{x,y} - p_{x,y}^{SP}} \right)^{2}}} = {\sum\limits_{x}{\sum\limits_{y}\left( {S_{x,y} - {\sum\limits_{i = 0}^{5}{\sum\limits_{j = 0}^{5}{h_{i,j}^{SP}P_{{\overset{\sim}{x} + i},{\overset{\sim}{y} + j}}}}}} \right)^{2}}}}} & (6)\end{matrix}$

For each of the sub-pixel positions a through o, MC unit 37 may set upan individual set of equations by computing the derivative of (e^(SP))²with respect to the filter coefficients h_(i,j) ^(SP). The number ofequations, in this case, is equal to the number of filter coefficientsused for the current sub-pixel position SP. For each two-dimensional(2D) sub-pixel position “e,” “f,” “g,” “i,” “j,” “k,” “m,” “n,” and “o,”prediction unit 32 may use a 6-by-6 tap 2D interpolation filter.Sub-pixel positions “e,” “f,” “g,” “i,” “j,” “k,” “m,” “n,” and “o,” are2D in the sense that they are not located in a vertical line orhorizontal line between two integer-pixel positions. In this case, asystem of thirty-six equations with thirty-six unknowns can be solved byMC unit 37. The remaining 1D sub-pixel positions “a,” “b,” “c,” “d,”“h,” and “l” may only require a 1D interpolation filter (e.g., 1D 6-tapfilter). In the case of a 1D 6-tap filter, a system of six equations canbe solved by MC unit 37.

$\begin{matrix}\begin{matrix}{0 = \frac{\left( {\partial{\mathbb{e}}^{SP}} \right)^{2}}{\partial h_{k,l}^{SP}}} \\{= {\frac{\partial}{\partial h_{k,l}^{SP}}\left( {\sum\limits_{x}{\sum\limits_{y}\left( {S_{x,y} - {\sum\limits_{i}{\sum\limits_{j}{h_{i,j}^{SP}P_{{\overset{\sim}{x} + i},{\overset{\sim}{y} + j}}}}}} \right)^{2}}} \right)}} \\{= {\sum\limits_{x}{\sum\limits_{y}{\left( {S_{x,y} - {\sum\limits_{i}{\sum\limits_{j}{h_{i,j}^{SP}P_{{\overset{\sim}{x} + i},{\overset{\sim}{y} + j}}}}}} \right)P_{{\overset{\sim}{x} + k},{\overset{\sim}{y} + l}}}}}}\end{matrix} & (7) \\{{\forall k},{l \in \left\{ {0;5} \right\}}} & \;\end{matrix}$Filter(s) 39 may represent one filter or a set of many different filtersthat may be used by MC unit 37 to generate the predictive data.

Thus, one example process of deriving and applying the AIF filters mayhave the following steps, which can be performed by prediction unit 32:

-   -   1. Estimate motion vectors (mvx,mvy) for every video block to be        coded. During motion estimation, a fixed interpolation filter        (e.g., the interpolation process of H.264/AVC) can be applied.    -   2. Using these motion vectors, accumulate prediction error        energy for each sub-pixel position SP over the current video        frame. Then, calculate adaptive filter coefficients h_(i,j)        ^(SP) for each sub-pixel position SP independently by minimizing        the prediction error energy as in the two prediction energy        equations above.    -   3. Estimate new motion vectors. During this motion estimation        process, the adaptive interpolation filters computed in step 2        may be applied. Using the adaptive interpolation filters, motion        estimation errors, caused by aliasing, camera noise, etc., are        reduced and better motion prediction is achieved.

Different AIF schemes may use the same analytical process as givenabove. The differences between the different schemes mostly lie in thenumber of unique filter coefficients used, whether the interpolationfilters are separable or non-separable, and the filter support used(i.e., integer pixel positions used to interpolate at least a portion ofthe sub-pixel positions). In each of these schemes, certain symmetryconstraints on the AIF filters may be imposed to reduce the number offilter coefficients that need to be encoded and sent in the videobitstream.

For NS-AIF, for example, MC unit 37 of prediction unit 32 mayinterpolate 1D sub-pixel positions “a,” “b,” “c,” “d,” “h,” and “l”using a 1D 6-tap interpolation filter (also referred to as a 6-positionfilter as each tap corresponds with a integer-pixel position), whichrequires six coefficients. The six coefficients of the 6-positionfilters used to interpolate the 1D sub-pixel each correspond with one ofthe integer-pixel positions illustrated in FIG. 3. For example, forsub-pixel positions “a,” “b,” and “c,” the six integer-pixel positionscorresponding to the coefficients are C1, C2, C3, C4, C5 and C6 and forsub-pixel positions “d,” “h,” and “l,” the six integer-pixel positionscorresponding to the coefficients are A3, B3, C3, D3, E3 and F3. Theseinteger-pixel positions represent the “filter support” of theinterpolation filter.

Prediction unit 32 may interpolate 2D sub-pixel positions “e,” “f,” “g,”“i,” “j,” “k,” “m,” “n,” and “o” using a 2D 6-by-6 interpolation filter,which requires thirty-six filter coefficients. The thirty-sixcoefficients of the 2D 6×6 interpolation filter used to interpolate the2D sub-pixel each correspond with integer-pixel positions A1-A6, B1-B6,C1-C6, D1-D6, E1-E6 and F1-F6. These integer-pixel positions representthe “filter support” of the interpolation filter. If no additionalrestriction is imposed, e.g., no coefficient or pixel symmetry, videoencoder 50 may encode and transmit nine sets of thirty-six coefficientsfor the 2D sub-pixel positions and six sets of six coefficients for the1D sub-pixel positions, for a total of 360 coefficients. Encoding andsending that number of coefficients in the video bitstream may result incostly bit overhead. The high bit overhead may, in turn, increase thebit rate for a given level of distortion, which is undesirable.

To reduce the bit overhead associated with sending the filtercoefficients, certain symmetry restrictions may be imposed on theinterpolation filters to reduce the number of unique filter coefficientsthat need to be sent to decoder 28. Two types of symmetry, i.e., pixelsymmetry and coefficient symmetry may be imposed, alone or incombination. Pixel symmetry enforces the same set of filter coefficients(and the mirrored, flipped and/or rotated versions of the set) fordifferent sub-pixel positions. Pixel symmetry may also be referred to assub-pixel symmetry insofar as such pixel symmetry applies with respectto the filter coefficients associated with two or more sub-pixellocations. Coefficient symmetry, on the other hand, enforces a giveninterpolation filter to be symmetric in a certain direction (e.g.,horizontal direction, vertical direction or both) for the various filtersupport positions relative to other filter support positions for a givensub-pixel values to be interpolated.

Pixel symmetry may be used in NS-AIF. Referring again to FIG. 3, let h₀^(a), h₁ ^(a), . . . , h₅ ^(a) be the set of filter coefficients forsub-pixel position “a,” then the set of filter coefficients forsub-pixel position “c” is h₅ ^(a), h₄ ^(a), . . . , h₀ ^(a), i.e., thesame coefficient in reverse order or horizontally flipped. That is,prediction pixel value p^(a) at sub-pixel position “a” and predictionpixel value p^(c) at sub-pixel position “c” may be calculated using (8)and (9), respectively.p ^(a) =h ₀ ^(a) ·C ₁ +h ₁ ^(a) ·C ₂ +h ₂ ^(a) ·C ₃ +h ₃ ^(a) ·C ₄ +h ₄^(a) ·C ₅ +h ₅ ^(a) ·C ₆  (8)p ^(c) =h ₅ ^(a) ·C ₁ +h ₄ ^(a) ·C ₂ +h ₃ ^(a) ·C ₃ +h ₂ ^(a) ·C ₄ +h ₁^(a) ·C ₅ +h ₀ ^(a) ·C ₆  (9)Sub-pixel positions “d” and “l” may be interpolated using the same setof interpolation filter coefficients as sub-pixel positions “a” and “c,”respectively. As such, sub-pixel positions “a” and “c” may have pixelsymmetry relative to sub-pixel positions “d” and “l.”

One aspect of this disclose is to actually remove pixel symmetry betweensub-pixel positions “a” and “c” relative to sub-pixel positions “d” and“l.” In addition, pixel symmetry may be avoided for sub-pixel position“f” relative to sub-pixel position “i.” In addition, pixel symmetry maybe avoided for sub-pixel position “k” relative to sub-pixel position“n.” In such cases, diagonal correlation may be low, making itinefficient or ineffective to impose diagonal symmetry in these cases.

As another example, let h_(0,0) ², h_(0,1) ^(e), . . . , h_(0,5) ^(e), .. . , h_(5,0) ^(e), h_(5,1) ^(e), . . . , h_(5,5) ^(e) be the set of6-by-6 2D filter coefficients for sub-pixel position “e.” Then the setof filter coefficients for position “g” is h_(0,5) ^(e), h_(0,4) ^(e), .. . , h_(0,4) ^(e), . . . , h_(5,5) ^(e), h_(5,4) ^(e), . . . , h_(5,0)^(e) (horizontally flipped version). Similarly, the set of filtercoefficients for sub-pixel position “m” is h_(5,0) ^(e), h_(5,1) ^(e), .. . , h_(5,5) ^(e), . . . , h_(0,0) ^(e), h_(0,1) ^(e), . . . , h_(0,5)^(e) (vertically flipped version), and the set of filter coefficientsfor sub-pixel position “o” is h_(5,5) ^(e), h_(5,4) ^(e), . . . ,h_(5,0) ^(e), . . . , h_(0,5) ^(e), h_(0,4) ^(e), . . . , h_(0,0) ^(e)(first horizontally flipped and then vertically flipped). As such,sub-pixel positions “e,” “g,” “m” and “o” have pixel symmetry. Symmetrybetween sub-pixel positions “b” and “h,” and between positions “f,” “i,”“k,” and “n” are imposed in a similar fashion as the examples givenabove. Under such a pixel symmetry constraint, there remain only fivegroups of unique filter sets, a first set of filter coefficients forsub-pixel group “a,” “c,” “d” and “l,” a second set of filtercoefficients for sub-pixel group “b” and “h,” a third set of filtercoefficients for sub-pixel group “e,” “g,” “m” and “o,” a fourth set offilter coefficients for group “f,” “i,” “k,” and “n,” and a fifth set offilter coefficients for sub-pixel group “j.”

Furthermore, NS-AIF may impose coefficient symmetry restriction on someof these filter sets, alone or in combination with the sub-pixelsymmetry described above. In some cases, diagonal coefficient symmetrymay be purposely avoided, e.g., eliminating any diagonal coefficientsymmetry constraints. In one instance, no coefficient symmetry isimposed on filter for the first group of sub-pixel positions, whichincludes sub-pixel locations “a,” “c,” “d” and “l.” However, the secondgroup of sub-pixel positions, which includes coefficient “b” and “h,”may have coefficient symmetry in accordance with equation (10).h ₀ ^(b) =h ₅ ^(b) , h ₁ ^(b) =h ₄ ^(b) , h ₂ ^(b) =h ₃ ^(b)  (10)Likewise, the coefficients for interpolation filters for the third,fourth and fifth groups of sub-pixel positions, which include sub-pixelpositions “e,” “f,” and “j,” respectively, may have coefficient symmetryas given in equations (11), (12), and (13), respectively.h _(i,j) ^(e) =h _(j,i) ^(e), for i,j=0 . . . 5, i≠j  (11)h _(i,j) ^(f) =h _(i,5-j) ^(f), for i,j=0 . . . 5  (12)h _(i,j) ^(j) =h _(i,5-j) ^(j) =h _(5-i,j) ^(j) =h _(5-i,5-j) ^(j), fori,j=0 . . . 2h _(i,j) ^(j) =h _(j,i) ^(j) i≠j  (13)

Under such pixel symmetry and coefficient symmetry constraints describedabove, the number of unique filter coefficients can be reduced from 360(no symmetry constraint) to 6(a)+3(b)+21(e)+18(f)+6(i)=54 coefficients,i.e., 6 coefficients for the group including sub-pixel position “a,” 3coefficients for the group including sub-pixel position “b,” 21coefficients for the group including sub-pixel position “e,” 18coefficients for the group including sub-pixel position “f,” and 6coefficients for the group including sub-pixel position “j.” Thus, inNS-AIF, video encoder 22 may encode and transmits fifty-fourcoefficients instead of 360, as in the case of no symmetry constraints.As described above, the fifty-four coefficients may be analyticallysolved based on equation (7) above. Video encoder 50 may then predict,quantize, code (e.g, using signed Exp-Golomb code), and send thecoefficients in the bitstream. Additional details on the predictivecoding of filter coefficients is discussed in greater detail below.Coefficient prediction and quantization unit 41 may be used for thepredictive coding and quantization of filter coefficients consistentwith this disclosure.

In another AIF scheme, i.e., S-AIF, prediction unit 32 may use separableinterpolation filters in the horizontal direction and in the verticaldirection, instead of non-separable interpolation filters as used inNS-AIF. For the 1D sub-pixel positions, prediction unit 32 (e.g., MCunit 37 of prediction unit 32) applies only horizontal directionalfilters or only vertical directional filters depending on the sub-pixellocation. In one example, the horizontal and vertical directionalfilters comprise 6-position (or 6-tap) filters. Prediction unit 32applies horizontal directional filters for sub-pixel positions “a,” “b,”and “c” with integer-pixel positions C1, C2, C3, C4, C5, and C6 (seeFIG. 3) as filter support and applies vertical directional filters forsub-pixel positions “d,” “h,” and “l” with integer-pixel positions A3,B3, C3, D3, E3 and F3 (see FIG. 3) as filter support. For the remainingsub-pixel positions, i.e., the 2D sub-pixel positions, prediction unit32 applies horizontal filtering first, followed by vertical filtering orvertical filtering, followed by horizontal filtering. Again, the filtercoefficients used for the separable horizontal and vertical filters maybe computed in accordance with equation (7) above. Without any symmetry,S-AIF may reduce the number of coefficients that need to be coded andsent to 144 coefficients from 360 coefficients, as is the case of NS-AIFwith no symmetry.

S-AIF may further impose one or more symmetry restrictions on at leastsome of the interpolation filters. Unlike the sub-pixel symmetry ofNS-AIF, with S-AIF, the filter sets used for sub-pixel positions “a” and“c” are not symmetric, i.e., horizontally flipped versions of eachother. Instead, the coefficients of the filter sets used for sub-pixelpositions “a” and “c” are derived independently of one another as h₀^(a), h₁ ^(a), . . . , h₅ ^(a) and h₀ ^(c), h₁ ^(c), . . . , h₅ ^(c),respectively. For each sub-pixel position that has vertical movement,the sub-pixel symmetry is similar to NS-AIF. That is, coefficients forsub-pixel positions “d” and “l,” coefficients for sub-pixel positions“e” and “m,” coefficients for sub-pixel positions “f” and “n,” andcoefficients for sub-pixel positions “g” and “o” are vertically flippedversions of each other, respectively. For example, if the coefficientsof the vertical filter for sub-pixel position “d” is h₀ ^(d), h₁ ^(d), .. . , h₅ ^(d), then the coefficients of the vertical filter forsub-pixel position “l” is h₅ ^(d), h₄ ^(d), . . . , h₀ ^(d). Thus, thesub-pixel symmetry used in S-AIF only has vertical sub-pixel symmetry inone dimension. S-AIF does not, however, have horizontal sub-pixelsymmetry. The sub-pixel symmetry in S-AIF may reduce the number ofcoefficients that need to be encoded and sent from 144 coefficients to102 coefficients.

S-AIF, like NS-AIF, may also use coefficient symmetry to further reducethe number of coefficients that need to be encoded and sent by videoencoder 50. The coefficient symmetry used in S-AIF may be the same as inNS-AIF, except that in S-AIF there are only 1D (horizontal or vertical)6-tap filters. In other words, there are no 6-by-6 2D filters. In S-AIF,for each sub-pixel position SP, SPε{b, h, i, j, k}, the filtercoefficients may have symmetry as indicated in equation (14).h ₀ ^(SP) =h ₅ ^(SP) , h ₁ ^(SP) =h ₄ ^(SP) , h ₂ ^(SP) =h ₃ ^(SP)  (14)

Therefore, with both sub-pixel symmetry and coefficient symmetry, S-AIFfilters may have eleven sets of unique 1D 6-tap filters, five of whichare symmetric filters that each have three unique coefficients. Theremaining six filters may each have six unique coefficients. In thiscase, video encoder 22 may quantize, predict, code, and send a total offifty-one unique filter coefficients in the video bitstream.

In another AIF scheme, D-AIF, prediction unit 32 uses directionalfilters for the 2D sub-pixel positions “e,” “f,” “g,” “i,” “j,” “k,”“m,” “n,” and “o.” The directional filters may comprise six tap filters,and coefficients may have symmetry so that only a subset of thecoefficients needs to be transmitted. As described herein, filtersupport refers to the pixel positions from the reference frame that isused in interpolating the sub-pixel positions. MC unit 37 may computesub-pixel positions “e” and “o” with a 6-position (or 6-tap) diagonalinterpolation filter that has integer pixel positions A1, B2, C3, D4, E5and F6 as filter support. Prediction unit 32 may compute sub-pixelpositions “g” and “m” with a 6-position diagonal interpolation filterthat has integer pixel positions A6, B5, C4, D3, E2 and F1 as filtersupport. Prediction unit 32 may compute sub-pixel positions “f,” “i,”“j,” “k” and “n” with a 12-position diagonal interpolation filter thathas integer pixel positions A1, B2, C3, D4, E5, F6, A6, B5, C4, D3, E2and F1 as filter support.

D-AIF may use the same sub-pixel and coefficient symmetry as describedabove with respect to NS-AIF. In terms of sub-pixel symmetry, there arefive unique sets of filter coefficients for interpolating the fifteensub-pixel positions with sub-pixel positions “a,” “c,” “d” and “l”sharing the same filter coefficients (e.g., mirrored, flipped and/orrotated versions), sub-pixel positions “e,” “g,” “m” and “o” sharing thesame filter coefficients (e.g., mirrored, flipped and/or rotatedversions), sub-pixel positions “b” and “h” sharing the same filtercoefficients (“b” being a horizontal filter and “h” being a verticalfilter), sub-pixel positions “f,” “i,” “k,” and “n” sharing the samefilter coefficients (e.g., mirrored, flipped and/or rotated versions),and sub-pixel position “j” has its own set of filter coefficients.

In terms of coefficient symmetry, filter coefficients for the firstsub-pixel group including “a,” “c,” “d” and “l,” uses a 1D 6-positionfilter having six unique coefficients (i.e., the first group beingnon-symmetric), filter coefficients for the second sub-pixel group “b”and “h” uses a 1D 6-position filter having three unique coefficients,(i.e., b is symmetric), and a third set of filter coefficients forsub-pixel group “e,” “g,” “m” and “o” uses a directional 6-positionfilter having six unique coefficients. A fourth set of filtercoefficients for group “f,” “i,” “k,” and “n” use a 12-tap filter having6 unique coefficients and a fifth set of filter coefficients for group“j” having three unique coefficients. The total number of uniquecoefficients in the D-AIF scheme is 6(a)+3(b)+6(e)+6(f)+3(j)=24coefficients. These filter coefficients may be predicted, quantized,coded, and sent in the video bitstream.

MC unit 37 of prediction unit 32 may use one or more of theinterpolation techniques described herein to enhance the accuracy ofinterpolation of sub-pixel positions and/or reduce the number ofinterpolation filter coefficients that need to be encoded andtransmitted. Described in greater detail below is a useful twelve-pixelfilter support with a radial shape that may be used for interpolation,techniques that use coefficient symmetry and pixel symmetry to reducethe amount of data needed to be sent between an encoder and a decoder toconfigure the filter support for interpolation, techniques for removingsome aspects of symmetry relative to conventional techniques, andtechniques for filtering data at integer pixel locations in a mannerthat is similar to sub-pixel interpolation. Offset techniques are alsodescribed. In addition, techniques for selecting between interpolationfilters, and techniques for predictively encoding filter coefficientsare also described.

FIG. 4 is a conceptual diagram illustrating a twelve pixel filtersupport with respect to nine sub-pixel locations. In accordance withthis disclosure, MC unit 37 may compute sub-pixel values for sub-pixelpositions “e,” “f,” “g,” “i,” “j,” “k,” “m,” “n,” and “o” based on theshaded integer pixel values. In this case, computing the sub-pixelvalues comprises applying an interpolation filter that defines atwo-dimensional array of filter support positions corresponding to a setof twelve (or possibly more) integer pixel positions that surround thesub-pixel positions as illustrated in FIG. 4. The filter supportpositions are shown with shading and capitalized lettering, and thesub-pixel positions are shown as boxes with dotted lines and lower caselettering. MC unit 37 may interpolate pixels in this manner for everypixel of a video block, and may generate a prediction block based oninterpolated the sub-pixel values. The 12 position filter support has aradial shape, and has similar frequency response as the 36 positionfilter support used in NS-AIF but with much reduced interpolationcomplexity.

As can be appreciated from FIG. 3, the sub-pixel values may comprisefifteen half-pel and quarter-pel values. The interpolation filter thatdefines the two-dimensional array of filter support positions is appliedto define nine of the fifteen half-pel and quarter-pel values as shownin FIG. 4.

The set of twelve or more integer pixel positions that surround thesub-pixel positions are shown with shading in FIG. 4, and may includefour integer-pixel positions C3, C4, D3 and D4 surrounding the ninehalf-pel and quarter-pel values, and eight integer-pixel positions B3,B4, C2, C5, D2, D5, E3 and E4 surrounding the four integer-pixelpositions. Each of the filter support positions in the two-dimensionalarray of filter support positions shown in FIG. 4 are within twointeger-pixel positions relative to the sub-pixel positions. Suchfiltering may form part of motion compensation during a video encodingprocess by video encoder 50 (FIG. 2) or during a video decoding processby video decoder 60 (FIG. 16). During the video encoding process,prediction unit 32 forward a plurality of sets of coefficient values toentropy coding unit 46 as part of the prediction syntax elements. Asshown in FIG. 4, the set of twelve or more integer pixel positions thatsurround the sub-pixel positions has a radial shape and may comprise aset of spatially closest integer pixel positions to the nine half-peland quarter-pel values defined by the interpolation filter (e.g.,filter(s) 39 in FIG. 2).

Entropy coding unit 46 may encode the sets of coefficient values andoutput the coefficient values of the interpolation filter as part of anencoded bitstream, which may then be transmitted to another device. Eachof the plurality of sets of coefficient values defined with respect toeach different sub-pixel location shown in FIG. 4 may define differentweightings for the twelve or more integer pixel positions. Pixelsymmetry may exist such that M sets of coefficient values define Ndifferent weightings for the twelve or more integer pixel positions,wherein M and N are positive integers and N is greater than M. In otherwords, pixel symmetry may allow some of the pixel locations to rely onmirrored, inverted or rotated weightings of other pixel locations sothat coefficients do not need to be coded for locations that have pixelsymmetry with other sub-pixel locations.

FIG. 5 is a conceptual diagram illustrating a horizontal six-pixelfilter support with respect three horizontal sub-pixel locations, and avertical six-pixel filter support with respect three vertical sub-pixellocations. The filter support locations are shaded with capitollettering, and the sub-pixel locations are un-shaded, with lower caselettering and dotted lines. MC unit 37 may compute the sub-pixel valuesfor locations “a,” “b,” and “c” by applying a linear interpolationfilter that defines a one-dimensional array of filter support positionsC1-C6. MC unit 37 may also compute the sub-pixel values for locations“d,” “h,” and “l” by applying a linear interpolation filter that definesa one-dimensional array of filter support positions A3-F3. In this way,linear interpolation filters may be used for six of the fifteen half-peland quarter-pel values shown in FIG. 3.

FIG. 6 is a conceptual diagram illustrating a five pixel-by-five pixelfilter support for filtering an integer pixel location. In this caseinterpolation-like filtering may be applied with respect to integerpixel locations in order to generate adjusted integer pixel values. InFIG. 6, for example, integer pixel value at position C3 may be filteredbased on a five-by-five pixel array A1-A5, B1-B5, C1-C5, D1-D5 andE1-E5. Other filter supports could be defined for integer filtering. Inthis case, if ME unit 35 identifies a block of integer pixels, MC unit37 may filter each pixel based on a five-by-five pixel array shown inFIG. 6 (or other filter support) to generate filtered integer pixelvalues. In this way, interpolation-like filtering may be applied tointeger pixels, which may improve encoding at times of illuminationchanges or scene fade-ins or fade-outs. In addition, integer filteringmay remove noise and facilitate image frame sharpening, and may helpimprove encoding of fine object movement between successive video framesparticularly when symmetry is not imposed on filter coefficients.Integer-pel filtering may also be extremely useful in improving qualityand/or compression for video sequences that have focus changes.

Thus, video encoder 22 and/or video decoder 28 may utilizeinterpolation-like filtering with respect to integer pixel positions inorder to generate adjusted integer pixel values. Video encoder 22 orvideo decoder 28 may, for example, obtain blocks of pixels, wherein theblocks of pixels includes integer pixel values corresponding to integerpixel positions within the block of pixels. Video encoder 22 or videodecoder 28 may filter the integer pixel values based on other integerpixel values within the blocks of pixels to generate adjusted integerpixel values, wherein the adjusted integer pixel values correspond tothe integer pixel positions, and generating a prediction block based onthe adjusted integer pixel values. The prediction block can then be usedto encode or decode a video block, depending upon whether the process isperformed during encoding or decoding.

If the technique is performed during an encoding process, video encoder22 may generate a residual video block as a difference between a currentvideo block being encoded and the prediction block. Video encoder 22 mayalso encode one set of filter coefficients per coded unit for integerfiltering, or possibly encode two or more sets of filter coefficientsper coded unit for integer filtering at different locations within thecoded unit. In the example of FIG. 6, the filter supports for theinteger filtering may define a two-dimensional filter, e.g., a 5-by-5filter with offset. Furthermore, coefficient symmetry may exist betweenat least some coefficients used for the filtering such that only asubset of the 25 different coefficients are needed to determine all 25coefficients for the filter. Many other filter sizes and shapes may beused in accordance with integer-pel filtering.

If the integer-pel filtering is performed during a decoding process, thetechnique may comprise generating a reconstructed video block as a sumof a residual video block associated with a current video block beingdecoded and the prediction block. In this case, the decoding device(e.g., destination device 16) may receive one set of filter coefficientsper coded unit for integer filtering, or may receive two or more sets offilter coefficients per coded unit for integer filtering at differentlocations within the coded unit. Each coded unit may have one or moresets of integer-pel filtering coefficients, as well as sets of sub-pelfiltering coefficients, e.g., eight sets that may be used to generateall fifteen sets for half-pel and quarter-pel positions.

Again, interpolation-like filtering of integer pixel values may improvecompression specifically during illumination changes, scene fade-ins orfade-outs, may remove noise and facilitate image frame sharpening, andmay help improve encoding of fine object movement between successivevideo frames particularly when symmetry is not imposed on filtercoefficients. Furthermore, integer-pel filtering is also found to beextremely useful, e.g., to improve compression, in video sequences thathave focus changes.

Conventionally, interpolation-like filtering is not performed forinteger-pixel positions in AIF. Instead, the corresponding integer-pixelposition in the reference frame is simply used “as is.” However, inaccordance with this disclosure, a filter may be used for integer-pixellocations in a manner that is similar to interpolations. Interpolationfiltering on integer-pixel positions is technically a misnomer, as thepixel values of integer-pixel positions already exist. Thus, thefiltering of integer-pixel positions is refereed to herein asinterpolation-like filtering, and may be viewed as simply filtering theinteger-pixel values to generate new adjusted pixel values.

Unlike interpolation filtering for sub-pixel positions, where the goalis to obtain the non-existent values in the reference frame, pixels atinteger positions in the reference frame already exists. Yet, applyingfiltering on the integer-pixel positions in the reference frame offers anumber of benefits. For example, when integer-pixel positions contain anoffset, filtering of the integer-pixel positions may allow bettercapture of illumination changes between frames. Additionally, filteringon the integer-pixel positions may allow the integer-pixel positions tocapture special effects such as fade-in and fade-out. Filtering on theinteger-pixel positions may also remove certain signal noises (such ascamera noise) and/or performs sharpening, if needed. Furthermore, if nosymmetry on the integer-pixel filter is imposed, the integer-pixelfilter may help capture fine object movement, such as object movementthat is not a multiple of quarter-pixel. Finally, integer pixelfiltering may also be useful in improving compression of video sequenceswhen focus changes occur.

In some instances, interpolation filtering of integer-pixel positionsmay be dependent upon motion vectors. In other words, MC unit 37 ofprediction unit 32 may adaptively apply the interpolation filteringbased on the motion vector identified by ME unit 35. For instance,interpolation filtering of integer-pixel positions may be performed whenthe motion vector points to an integer-pixel position. The interpolationfiltering may result in an adjusted pixel value for the integer-pixelposition. When the motion vector points to a sub-pixel position, nofiltering of integer-pixel positions is performed with respect to theinteger pixel values, but filtering may be used for interpolation of thesub-pixel values. In this manner, integer-pixel filtering may beconsidered adaptive based on motion vectors.

For integer-pixel positions, a 5-by-5 interpolation filter in the formof equation (15) may be derived using the same analytical process usedto derive the sub-pixel position filters.

$\begin{matrix}{{p^{FP} = {{\sum\limits_{i = 0}^{4}{\sum\limits_{j = 0}^{4}{P_{i,j}h_{i,j}^{FP}}}} + o^{FP}}},} & (15)\end{matrix}$where p^(FP) is the filtered integer pixel value, P_(i,j) is the integerpixel value at position (i, j), h^(FP) _(i,j) is the coefficient forposition (i, j), and o^(FP) is a filter offset. In some instances, theinterpolation filter for integer-pixel positions may not have a filteroffset (i.e., o^(FP)=0). In other instances, the filter offset o^(FP)may use any of a number of techniques. Different offsets may be definedfor each sub-pixel position, e.g., fifteen different offsets may bedefined for each sub-pixel position shown in FIG. 3. Another offset maybe defined for integer-pixels, bringing the total number of offsets tosixteen. Co-pending and commonly assigned U.S. patent application Ser.No. 12/420,280, filed for Karczewicz et al., and entitled “OFFSETS ATSUB-PIXEL RESOLUTION”, which was filed on the same day as the presentapplication, and which is incorporated herein by reference, providesmany additional details on the use of many offsets for different pixeland sub-pixel locations.

In order to reduce the number of filter coefficients to be transmitted,coefficient symmetry as in equation (16) may be imposed on theinteger-pixel filter.h _(i,j) ^(FP) =h _(i,4-j) ^(FP) =h _(4-i,j) ^(FP) =h _(4-i,4-j) ^(FP),for i,j=0 . . . 4  (16)Other forms of coefficient symmetry may also be used. Whethercoefficient symmetry is imposed may be decided by the encoder andsignaled to the decoder. If the symmetry as in (16) is used on theinteger-pixel filter, then 9 filter coefficients and 1 offset may needto be sent to the decoder.

Coding of the offsets may be done by first coding an integer offset andthen coding each fractional offset. In this case, the integer offset andthe fractional offsets are coded differently. The integer offset may becoded using signed exponential-Golomb code. Depending on the magnitudeof the integer offset, the fractional offsets may be quantizeddifferently than the integer offset. For example, coarser levels ofquantization may be applied on the fractional offsets when an integeroffset has a large magnitude. After quantization, the fractional offsetsmay be coded using fixed length codes. As an example, the followingpseudo-code may illustrate how a given fractional offset may be codedusing different fixed length codes defined based on the integer offsetvalue.

Let offsetI be the integer offset Let offsetF be the fractional offsetLet offsetFbits be the number of bits used to quantize offsetF Letabs(a) be the absolute value of a   if(abs(offsetI) = 0)     offsetFbits= 5   else if(abs(offsetI) < 3)     offsetFbits = 4   elseif(abs(offsetI) < 7)     offsetFbits = 2   else     offsetFbits = 0

In this case, video encoder 50 may use coefficient prediction andquantization unit 41 that for each of the offset values assigns a firstnumber of bits to an integer portion of a given offset value and assignsa second number of bits to a fractional portion of the given offsetvalue, wherein the first and second numbers of bits are determined basedon a magnitude of the integer portion. In this case, the quantizedversions of the offset values may be forwarded from prediction unit 32as prediction syntax elements after quantization of the offset values isperformed consistent with the pseudo-code above. In any case, videoencoder 50 may encode the first number of bits differently than thesecond number of bits.

FIG. 7 is a conceptual diagram illustrating four integer pixel positionsand fifteen sub-pixel positions with shading to group pixel positionsthat may use pixel symmetry for filter coefficients consistent with thisdisclosure. In particular, FIG. 7 illustrates sub-pixel positions “a”through “o” corresponding to integer pixel position C3. The sub-integerpixel positions shown in FIG. 7 correspond to those of FIGS. 4 and 5.That is, sub-pixel locations “e,” “f,” “g,” “i,” “j,” “k,” “m,” “n,” and“o” may have values determined by a twelve pixel support as shown inFIG. 4, whereas sub-pixel locations “a,” “b,” and “c,” and sub-pixellocations “d,” “h,” and “l,” may have values determined by horizontaland linear pixel supports shown in FIG. 5.

In this scenario, pixel symmetry may be imposed such that the filtercoefficients for pixel location “a” are symmetry with respect to thefilter coefficients for pixel location “c.” Similarly, pixel symmetrymay be imposed such that the filter coefficients for pixel location “d”are symmetric with respect to the filter coefficients for pixel location“l.” Pixel symmetry may be imposed such that the filter coefficients forpixel location “e” are symmetric with respect to the filter coefficientsfor pixel locations “g,” “m” and “o,” and pixel symmetry may be imposedsuch that the filter coefficients for pixel location “i” are symmetricwith respect to the filter coefficients for pixel locations “k,” andpixel symmetry may be imposed such that the filter coefficients forpixel location “f” are symmetric with respect to the filter coefficientsfor pixel locations “n.” Therefore, only eight sets of coefficientvalues may need to be communicated as part of a video bitstream in orderto enable a decoder to generate complete sets of coefficient values forall fifteen pixel locations. Furthermore, for any given set ofcoefficients, coefficient symmetry may allow only a subset of thecoefficient values to be sent, and the decoder can generate the completeset of coefficient values for a given pixel location based on the subsetand coefficient symmetry.

One aspect of this disclose is to actually remove any pixel symmetrybetween sub-pixel positions “a” and “c” relative to sub-pixel positions“d” and “l.” In addition, pixel symmetry may be avoided for sub-pixelposition “f” relative to sub-pixel position “i.” In addition, pixelsymmetry may be avoided for sub-pixel position “k” relative to sub-pixelposition “n.” In such cases, diagonal correlation may be low, making itinefficient or ineffective to impose diagonal symmetry. In this manner,the eight sets of coefficients used to generate all fifteen sets ofcoefficients may be a larger set than some conventional techniquesinsofar as symmetry is avoided in the diagonal dimension for some of thepixel locations. In some cases, diagonal pixel symmetry and diagonalcoefficient symmetry may be eliminated or avoided to allow for variancein the vertical dimensions.

Referring again to FIG. 1, video encoder 22 may determine eight sets offilter coefficients and use the eight sets of filter coefficients togenerate all fifteen sets of filter coefficients corresponding tofifteen different sub-pixel locations based on coefficient symmetry andpixel symmetry, and output the eight sets of filter coefficients as partof an encoded bitstream. Eight sets of filter coefficients may be outputfor every coded unit of the encoded bitstream, or possibly severaloccurrences of the eight sets of filter coefficients may be output fordifferent areas of a coded unit.

The fifteen sets correspond to every half-pel and quarter-pel sub-pixellocation shown in FIG. 3, and the eight sets, the coefficient symmetryand the pixel symmetry define filter supports for every half-pel andquarter-pel sub-pixel location. Video decoder 28 of destination device16 may receive eight sets of filter coefficients as part of an encodedvideo bitstream, generate fifteen sets of filter coefficientscorresponding to fifteen different sub-pixel locations based on theeight sets of filter coefficients, generate interpolated predictive datafor video decoding based on one of the fifteen sets of filtercoefficients, wherein the interpolated predictive data corresponds toone of the fifteen different sub-pixel locations, and decode one or morevideo blocks base on the interpolated predictive data.

At least some of the eight sets of filter coefficients may havecoefficient symmetry such that a given set with coefficient symmetrycomprises a subset of the filter coefficients associated with a filtersupport for a given sub-pixel location associated with the given set.Thus, video decoder 28 may generate the fifteen sets of filtercoefficients based at least in part on pixel symmetry between two ormore different sub-pixel locations. Moreover, video decoder 28 maygenerate the fifteen sets of filter coefficients based at least in parton pixel symmetry between two or more different sub-pixel locations, andbased at least in part on coefficient symmetry defined for a given setsuch that the given set with coefficient symmetry comprises a subset ofthe filter coefficients associated with a filter support for a givensub-pixel location associated with the given set.

Again, the fifteen sets may correspond to every half-pel and quarter-pelsub-pixel location. Pixel symmetry may exist between different ones ofthe fifteen sets of filter coefficients in a vertical dimension and in ahorizontal dimension, but pixel symmetry does not exist in a diagonaldimension for at least some of the fifteen sets of filter coefficients.As explained in greater detail below, two of the eight sets may definethree of the fifteen sets for a horizontal filter support, two of theeight sets may define three of the fifteen sets for a vertical filtersupport, and four of the eight sets may define nine of the fifteen setsfor a two-dimensional filter support. The two-dimensional support maycomprise a useful 12 position filter support surrounding nine sub-pixelpositions in a radial shape.

Specifically, the interpolated predictive data may comprise fifteenhalf-pel and quarter-pel values, e.g., shown in FIG. 7, wherein theinterpolation filter defines a two-dimensional array of twelve filtersupport positions, e.g., shown in FIG. 4, that are applied to definenine of the fifteen half-pel and quarter-pel values and, e.g., alsoshown in FIG. 4. Generating the interpolated predictive data may furthercomprise applying linear interpolation filters that defineone-dimensional arrays of filter support positions for six of thefifteen half-pel and quarter-pel values, e.g., as shown in FIG. 5.

Horizontal symmetry and lack of diagonal symmetry can be seen withrespect to sub-pixel positions “a” and “c” or with respect to sub-pixelpositions “i” and “k.” In this case, sub-integer pixel positions “a” and“c” are symmetric along a horizontal X axis, but symmetry does not existalong a diagonal axis for these pixels such that sub-pixel locations “d”and “l” are not symmetric with respect to sub-pixel positions “a” and“c” respectively. Similarly, sub-pixel positions “i” and “k” aresymmetric along a horizontal X axis, but symmetry does not exist alongdiagonal axis for these pixels such that sub-pixel locations “f” and “n”are not symmetric with respect to sub-pixel positions “i” and “k”respectively.

The pixel symmetry means that a first set of coefficients with respectto the first sub-pixel position are symmetric relative to a second setof coefficients with respect to the second sub-pixel position. Forexample, pixel location “a” has pixel symmetry with respect to pixellocation “c,” and pixel location “d” has pixel symmetry with respect topixel location “l.” Pixel location “f” has pixel symmetry with respectto pixel location “n,” and pixel location “i” has pixel symmetry withrespect to pixel location “k.” Pixel location “e” has pixel symmetrywith respect to pixel locations “g,” “m,” and “o.” The shading shown inFIG. 7 demonstrates this pixel symmetry, e.g., with common shadingcorresponding to pixel locations that are symmetric with respect toother pixel locations. In this case, coefficients for eight pixellocations (together with aspects of pixel symmetry) may definecoefficients for all fifteen pixel locations. In addition, within agiven set of coefficients, coefficient symmetry may exist such that onlya subset of the coefficients in that set of coefficients needs to becommunicated with the bitstream.

Video encoder 22 can generate encoded video data based on interpolatedpredictive data, wherein the interpolated predictive data comprisesfifteen half-pel and quarter-pel values, wherein the interpolationfilter defines a two-dimensional array of twelve filter supportpositions that are applied to define nine of the fifteen half-pel andquarter-pel values. Generating the encoded video data based oninterpolated predictive data may further comprise applying linearinterpolation filters that define one-dimensional arrays of filtersupport positions for six of the fifteen half-pel and quarter-pelvalues.

FIG. 8 is a conceptual diagram illustrating six horizontal linear pixelsupport positions C1-C6 relative to a sub-pixel position “b,” withshading that shows coefficient symmetry. In this case, coefficientsymmetry means that only three filter coefficients are needed for C1, C2and C3 in order to define the entire set of coefficients for filtersupport positions C1-C6. C1 is symmetric with C6, C2 is symmetry with C5and C3 is symmetric with C4. Thus, only three coefficients need to becommunicated as part of an encoded video bitstream in order to definethe set of six coefficients needed to interpolate sub-pixel position“b.”

FIG. 9 is a conceptual diagram illustrating six horizontal linear pixelsupport positions relative to a sub-pixel, with shading that shows alack of any coefficient symmetry. Thus, all six coefficients are neededin order to define the set of coefficients for filter support withrespect to sub-pixel position “a.” As noted above, however, pixelsymmetry means that these same coefficients with respect to sub-pixelposition “a” can also be used to derive the filter support for sub-pixelposition “c” (see FIG. 7). Thus, only six coefficients need to becommunicated as part of an encoded video bitstream in order to definetwo different sets of six coefficients needed to interpolate sub-pixelposition “a” and “c.”

FIG. 10 is a conceptual diagram illustrating six vertical linear pixelsupport positions A3, B3, C3, D3, E3 and F3 relative to a sub-pixel “h,”with shading that shows coefficient symmetry. In this case, coefficientsymmetry means that only three filter coefficients are needed for A3, B3and C3 in order to define the entire set of coefficients for filtersupport positions A3, B3, C3, D3, E3 and F3. A3 is symmetric with F3, B3is symmetry with E3 and C3 is symmetric with D3. Thus, only threecoefficients need to be communicated as part of an encoded videobitstream in order to define the set of six coefficients needed tointerpolate sub-pixel position “h.”

FIG. 11 is a conceptual diagram illustrating six vertical linear pixelsupport positions relative to a sub-pixel, with shading that shows alack of any coefficient symmetry.

Thus, all six coefficients are needed in order to define the set ofcoefficients for filter support with respect to sub-pixel position “d.”As noted above, however, pixel symmetry means that these samecoefficients with respect to sub-pixel position “d” can also be used toderive the filter support for sub-pixel position “l” (see FIG. 7). Thus,only six coefficients need to be communicated as part of an encodedvideo bitstream in order to define two different sets of sixcoefficients needed to interpolate sub-pixel position “d” and “l.”

FIG. 12 is a conceptual diagram illustrating twelve two-dimensionalpixel support positions (the integer pixel positions shown with capitollettering and shading) relative to a sub-pixel position “e.” The shadingthat shows a lack of any coefficient symmetry. Thus, all twelvecoefficients are needed in order to define the set of coefficients forfilter support with respect to sub-pixel position “e.” As noted above,however, pixel symmetry means that these same coefficients with respectto sub-pixel position “e” can also be used to derive the filter supportfor sub-pixel positions “g,” “m,” and “o” (see FIG. 7). Therefore, 12coefficients define the filter support sub-pixel position “e” and thesesame coefficients can be used to derive the set of coefficients forsub-pixel positions “g,” “m,” and “o.”

FIG. 13 is a conceptual diagram illustrating twelve two-dimensionalpixel support positions (the integer pixel positions shown with capitollettering) relative to a sub-pixel “i,” with shading that showscoefficient symmetry. In this case, coefficient symmetry means that thefilter coefficients for filter support positions B3, B4, C2, C3, C4 andC5 can be used to define the filter coefficients for filter supportpositions E3, E4, D2, D3, D4 and D5. B3 is symmetric with E3, B4 issymmetry with E4, C2 is symmetric with D2, C3 is symmetry with D3, C4 issymmetric with D4, and C5 is symmetry with D5. Thus, only sixcoefficients need to be communicated as part of an encoded videobitstream in order to define the set of twelve coefficients needed tointerpolate sub-pixel position “i.” Furthermore, as noted above,sub-pixel position “i” may have pixel symmetry with respect to sub-pixelposition “k.” Therefore, the same subset of six coefficients can definethe entire sets of twelve filter coefficients for sub-pixel positions“i” and “k.”

FIG. 14 is a conceptual diagram illustrating twelve two-dimensionalpixel support positions (the integer pixel positions shown with capitollettering) relative to a sub-pixel “f,” with shading that showscoefficient symmetry. In this case, coefficient symmetry means that thefilter coefficients for filter support positions C2, D2, B3, C3, D3 andE3 can be used to define the filter coefficients for filter supportpositions C5, D5, B4, C4, D4 and E4. C2 is symmetric with C5, D2 issymmetry with D5, B3 is symmetric with B4, C3 is symmetry with C4, D3 issymmetric with D4, and E3 is symmetry with E4. Thus, only sixcoefficients need to be communicated as part of an encoded videobitstream in order to define the set of twelve coefficients needed tointerpolate sub-pixel position “f.” Furthermore, as noted above,sub-pixel position “f” may have pixel symmetry with respect to sub-pixelposition “n.” Therefore, the same subset of six coefficients can definethe entire sets of twelve filter coefficients for sub-pixel positions“f” and “n.”

FIG. 15 is a conceptual diagram illustrating twelve two-dimensionalpixel support positions (the integer pixel positions shown with capitollettering) relative to a sub-pixel “j,” with shading that showscoefficient symmetry. In this case, coefficient symmetry means that onlythree filter coefficients are needed for B3, C2 and C3 in order todefine the entire set of twelve coefficients for filter supportpositions B3, B4, C2, C3, C4, C5, D2, D3, D4, D5, E3 and E4. C3 issymmetric with C4, D3 and D4, C2 is symmetry with D2, C and E4, and B3is symmetric with B4, E3 and D5. Thus, only three coefficients need tobe communicated as part of an encoded video bitstream in order to definethe set of twelve coefficients needed to interpolate sub-pixel position“j.”

FIG. 16 is a block diagram illustrating an example of a video decoder,which may decode a video sequence that is encoded in the mannerdescribed herein. Video decoder 60 is one example of a specialized videocomputer device or apparatus referred to herein as a “coder.” Videodecoder 60 includes an entropy decoding unit 52 that entropy decodes thereceived bitstream to generate quantized coefficients and the predictionsyntax elements. The prediction syntax elements may include a codingmode, one or more motion vectors, information identifying aninterpolation technique use to generate the sub-pixel data, coefficientsfor use in interpolation filtering, and/or other information associatedwith the generation of the prediction block.

The prediction syntax elements, e.g., the coefficients, are forwarded toprediction unit 55. If prediction is used to code the coefficientsrelative to coefficients of a fixed filter, or relative to one another,coefficient prediction and inverse quantization unit 53 can decode thesyntax elements to define the actual coefficients. Also, if quantizationis applied to any of the prediction syntax, coefficient prediction andinverse quantization unit 53 can also remove such quantization. Filercoefficients, for example, may be predictively coded and quantizedaccording to this disclosure, and in this case, coefficient predictionand inverse quantization unit 53 can be used by video decoder 60 topredictively decode and de-quantize such coefficients.

Prediction unit 55 may generate prediction data based on the predictionsyntax elements and one or more previously decoded blocks that arestored in memory 62, in much the same way as described in detail abovewith respect to prediction unit 32 of video encoder 50. In particular,prediction unit 55 may perform one or more of the interpolationfiltering techniques of this disclosure during motion compensation togenerate a prediction block with a particular precision, such asquarter-pixel precision. As such, one or more of the techniques of thisdisclosure may be used by video decoder 60 in generating a predictionblock. Prediction unit 55 may include a motion compensation unit thatcomprises filters used for interpolation and interpolation-likefiltering techniques of this disclosure. The motion compensationcomponent is not shown in FIG. 16 for simplicity and ease ofillustration.

Inverse quantization unit 56 inverse quantizes, i.e., de-quantizes, thequantized coefficients. The inverse quantization process may be aprocess defined for H.264 decoding. Inverse transform unit 58 applies aninverse transform, e.g., an inverse DCT or conceptually similar inversetransform process, to the transform coefficients in order to produceresidual blocks in the pixel domain. Summer 64 sums the residual blockwith the corresponding prediction block generated by prediction unit 55to form a reconstructed version of the original block encoded by videoencoder 50. If desired, a deblocking filter may also be applied tofilter the decoded blocks in order to remove blockiness artifacts. Thedecoded video blocks are then stored in reference frame store 62, whichprovides reference blocks for subsequent motion compensation and alsoproduces decoded video to drive display device (such as device 28 ofFIG. 1).

NS-AIF techniques may compute pixel values for the 2D sub-pixelpositions using an interpolation filter having 36-postion filter support(i.e., a 2D 6×6 filter support). S-AIF uses separable integer pixelpositions as filter support for interpolation filtering first in ahorizontal direction than in a vertical dimension. D-AIF uses a lesscomplex interpolation filter for computing the 2D sub-pixel positionsthan NS-AIF. In D-AIF the 2D sub-pixel positions are computed using aninterpolation filter having either a 6-postion filter support or a12-position diagonal filter support.

One drawback of using the diagonal filter support as in D-AIF is thatthe integer-pixel positions used in filtering are far away from thecurrent position to be interpolated. As distance between the currentposition to be interpolated and the positions of the pixels used asfilter support increases, spatial pixel correlation decreases. As such,the diagonal filter support used in D-AIF is less suitable for formingan accurate prediction.

In order to provide better prediction using pixels with highercorrelation with the position to be interpolated (that is, pixelpositions that are closer by or a shorter distance from the position tobe interpolated), while maintaining the same low complexity offered byD-AIF, the 12-position filter support shown in FIG. 4 may be used forinterpolation. The integer-pixel positions used in the 12-positionfilter support described in this disclosure include the fourinteger-pixel positions surrounding the sub-pixel positions, i.e.,integer-pixel positions C3, C4, D3 and D4, which may be referred to as“corner” integer-pixel positions as they are located near the corners ofthe 2D sub-pixel positions. In addition to the corner integer-pixelpositions, one or more integer-pixel positions immediately adjacent tothe corner integer-pixel positions may also be used in the filtersupport. The integer-pixel positions immediately adjacent to the cornerinteger-pixel positions may include integer-pixel positions directlyabove (toward the top or north) or directly below (toward the bottom orsouth) the corner integer-pixel positions as well as integer-pixelpositions directly to the left (or west) or directly to the right (oreast) of the corner integer-pixel positions. In the example illustratedin FIG. 4, the 12-position filter support may include integer-pixellocations B3, B4, C2, C3, C4, C5, D2, D3, D4, D5, E3 and E4. All ofthese integer-pixel positions are located within two integer-pixelpositions of the sub-pixel to be interpolated.

Filter support in accordance with the techniques of this disclosure may,however, include additional integer-pixel locations. For example, thefilter support may include integer-pixel locations that are locatedwithin three integer-pixel positions of the sub-pixel position to beinterpolated that form a radial shape.

Even quarter-pixel positions or finer resolution may be interpolatedbased on integer pixel positions located within at least twointeger-pixel positions. In this manner, the filter support techniquesof this disclosure provide better interpolation prediction usinginteger-pixels with higher correlation with the position to beinterpolated, while maintaining the same low complexity offered byD-AIF.

As described above, sub-pixel symmetry may enforce the same set offilter coefficients (and the mirrored, inverted and/or rotated versionsof the set) for a group of one or more sub-pixel positions. Using ananalytical process, such as that in equation (5) may be used to deriveinterpolation filter coefficients for all sub-pixel positions, e.g.,fifteen sub-pixel positions in the case of quarter-pixel precision.Analysis of correlation among the fifteen sets of interpolation filtercoefficients reveals a relatively strong correlation between filtercoefficients of sub-pixel positions (both 1D and 2D sub-pixel positions)in the same dimension (e.g., vertical or horizontal dimension). To thecontrary, correlation between filter coefficients of the sub-pixelpositions in different diagonal directions may be noticeably weaker.

For example, there may be a high correlation between filter coefficientsfor sub-pixel positions “a” and “c,” which are both in the horizontaldimension. As another example, there may be a high correlation betweenfilter coefficients for sub-pixel positions “d” and “l,” which are bothin the vertical dimension. However, there may be little, if any,correlation between filter coefficients for sub-pixel positions “a,”which is in the horizontal dimension, and sub-pixel position “d,” whichis in the vertical dimension. Based on these observations of thecorrelations, sub-pixel symmetry may be designed such that sub-pixelsymmetry for filter coefficients of sub-pixel positions in diagonaldimensions is not imposed. This leads to eight sets of coefficients, asdescribed herein, which may be more sets than needed for someconventional processes. By eliminating diagonal pixel symmetry for somepixel locations, as described herein, video encoding and compression maybe improved.

Again, FIG. 7 shows pixel positions with the same shadings (orhatchings) that belong to a group of sub-pixel positions that havesub-pixel symmetry. In particular, sub-pixel positions “a” and “c” forma first group having sub-pixel symmetry, sub-pixel positions “d” and “l”form a second group having sub-pixel symmetry, sub-pixel positions “e,”“g,” “m,” and “o” form a third group having sub-pixel symmetry,sub-pixel positions “f” and “n” form a fourth group having sub-pixelsymmetry, and sub-pixel positions “i” and “k” form a fifth group havingsub-pixel symmetry. Sub-pixel positions “b,” “h,” and “j” do not havesub-pixel symmetry with any other sub-pixel positions. As such,sub-pixel positions “b,” “h,” and “j” may be viewed as belonging totheir own groups; a sixth, seventh and eighth group, respectively. Eachgroup of coefficients (which may be subsets due to coefficient symmetry)may be communicated once per coded unit, or possibly multiple times percoded unit if different types of filtering is defined for differentareas or features of a coded unit. Coefficients for integer-pelinterpolation-like filtering may also be sent once or several times percoded unit.

In NS-AIF and D-AIF sub-pixel symmetry exists between sub-pixelpositions in the same dimension, e.g., two sub-pixel positions in thevertical dimension or two sub-pixel positions in the horizontaldimension. In some conventional NS-AIF and D-AIF, symmetry also existsdiagonally between sub-pixel positions in different dimensions. Forexample, sub-pixel symmetry exists between sub-pixel position “a” in thevertical dimension and sub-pixel position “d” in the horizontaldirection in some conventional NS-AIF and D-AIF. With sub-pixel symmetryin horizontal, vertical and diagonal dimensions, as few as five uniquesets of interpolation filter coefficients may be used for quarter-pixelprecision.

For some types of S-AIF, on the other hand, sub-pixel symmetry existsbetween sub-pixel positions in the vertical direction (or dimension),but not sub-pixel positions in the horizontal dimension. In other words,sub-pixel symmetry does not always exist for sub-pixel positions in thesame dimension. Sub-pixel symmetry also does not exist in diagonaldimensions. The symmetry of some S-AIF schemes, thus, require more setsof interpolation coefficients. In particular, for quarter-pixelprecision, some S-AIF schemes require eleven unique sets ofinterpolation coefficients.

The sub-pixel symmetry scheme described in this disclosure, andillustrated in FIG. 7, may result in more accurate prediction than thesub-pixel symmetry described above for some types of NS-AIF and D-AIF.In particular, the sub-pixel symmetry scheme of FIG. 7 imposes sub-pixelsymmetry in one dimension (e.g., in the horizontal direction or thevertical direction), but does not impose sub-pixel symmetrysimultaneously in both dimensions (e.g., diagonally).

By not imposing sub-pixel symmetry for sub-pixel positions diagonally,the weak correlation between the interpolation filter coefficients andthe sub-pixel positions in different dimensions is not integrated intothe interpolation. Although more sets of interpolation coefficients maybe required (e.g., eight instead of five), the resulting interpolatedprediction data may be more accurate. Diagonal coefficient symmetry mayalso be avoided or eliminated, in much the same way.

Prediction unit 32 may also impose coefficient symmetry as describedabove. In particular, coefficient symmetry is imposed for filtercoefficients in one dimension (e.g., the horizontal or verticaldirection), but not for both dimensions in a diagonal manner. Forexample, the filter coefficients for sub-pixel position “e” are notforced to be diagonally symmetric as in the NS-AIF scheme describedabove and represented in equation (11). The coefficient symmetry issummarized below in equations (17)-(21).h ₀ ^(b) =h ₅ ^(b) , h ₁ ^(b) =h ₄ ^(b) , h ₂ ^(b) =h ₃ ^(b)  (17)h ₀ ^(h) =h ₅ ^(h) , h ₁ ^(h) =h ₄ ^(h) , h ₂ ^(h) =h ₃ ^(h)  (18)h _(i,j) ^(f) =h _(i,3-j) ^(f), for i,j=0 . . . 3  (19)h _(i,j) ^(i) =h _(3-i,j) ^(i), for i,j=0 . . . 3  (20)h _(i,j) ^(i)=h_(i,3-j) ^(j)=h_(3-i,j) ^(j) =h _(3-i,3-j) ^(j), fori,j=0 . . . 3  (21)Note that for sub-pixel positions “f,” “i,” and “j,” some filtercoefficients, i.e., h_(0,0) ^(SP)=h_(0,3) ^(SP)=h_(3,0) ^(SP)=h_(3,3)^(SP) are equal to zero in instances in which the 12-position filterdescribed in detail with respect to FIG. 4 may be used. As such, thesub-pixel and coefficient symmetry described in this disclosure may beused in conjunction with or separately from the filter supporttechniques. When used in conjunction with the 12-position filter supportdescribed in FIG. 4, there are6(a)+3(b)+6(d)+3(h)+12(e)+6(f)+6(i)+3(j)=45 unique coefficients forthese sub-pixel positions that need to be sent to the decoder, i.e., 6coefficients for the group including sub-pixel position “a,” 3coefficients for the group including sub-pixel position “b,” 6coefficients for the group including sub-pixel position “d,” 3coefficients for the group including sub-pixel position “h,” 12coefficients for the group including sub-pixel position “e,” 6coefficients for the group including sub-pixel position “f,” 6coefficients for the group including sub-pixel position “i,” and 3coefficients for the group including sub-pixel position “j.”

FIG. 17 is a flow diagram illustrating example operation of a videoencoder that utilizes a twelve pixel filter support consistent with thisdisclosure. The technique of FIG. 17 will be described from theperspective of video encoder 50 of FIG. 2 although other devices mayperform similar techniques. As shown, in FIG. 17, MC unit 37 ofprediction unit 32 obtains a block of pixels from memory 34 that includeinteger pixel values corresponding to integer pixel positions (171).Filter(s) 39 compute sub-pixel values based on a filter support thatcomprises twelve or more positions that surround the sub-pixel positions(172). As explained in greater detail above, the twelve-position filtersupport may be used to generate nine of the fifteen possible sub-pixelinterpolations, while linear horizontal and linear vertical filteringmay be used to generate six of the fifteen possible sub-pixelinterpolations.

MC unit 37 then generates a prediction block based on the computedsub-pixel values (173). In particular, MC unit 37 may generate andoutput an interpolated prediction block comprising interpolatedsub-pixel values. Adder 48 can then encode a current video block basedon the interpolated prediction block (174), e.g., by subtracting theinterpolated prediction block from the video block being encoded togenerate a residual block. The residual block may then be transformedand quantized by transform unit 38 and quantization unit 40,respectively. Following entropy coding by entropy coding unit 46, videoencoder 50 can output an encoded video bitstream and filter information(175). The filter information, as described herein, may comprise eightsets of coefficients used for generating all fifteen sets ofcoefficients for fifteen sub-pel positions. The filter information maybe output once per coded unit, or possibly several times per coded unitif different areas of a coded unit use different types of sub-pelinterpolation.

FIG. 18 is a flow diagram illustrating example operation of a videodecoder that utilizes a twelve pixel filter support consistent with thisdisclosure. Thus, the process of FIG. 18 may be considered thereciprocal decoding process to the encoding process of FIG. 17. FIG. 18will be described from the perspective of video decoder 60 of FIG. 16although other devices may perform similar techniques. As shown in FIG.18, video decoder 60 receives an encoded video blocks and filterinformation (181). Entropy decoding unit 52 may entropy decode thisreceived information. Prediction unit 55 performs interpolative motioncompensation according to techniques of this disclosure. In particular,prediction unit 55 obtains a block of pixels from memory 62 that includeinteger pixel values corresponding to integer pixel positions (182).Prediction unit 55 may use received motion vectors to determine how toperform interpolation. Based on the motion vectors, prediction unit 55can compute sub-pixel based on a filter support that comprises twelve ormore positions that surround the sub-pixel positions (183). In this way,prediction unit 55 uses interpolation to generate the prediction block(184). The prediction block may be used to decode a residual video blockby invoking adder 64 to add the prediction block to the residual block(185). The various aspects of interpolation described herein, includingthe use of a twelve position filter support that surrounds the sub-pixelpositions in a radial shape, may improve video encoding by providingbetter quality interpolate data than conventional techniques.

FIG. 19 is a flow diagram illustrating example operation of a videoencoder that utilizes coefficient symmetry and pixel symmetry consistentwith this disclosure. The technique of FIG. 19 will be described fromthe perspective of video encoder 50 of FIG. 2 although other devices mayperform similar techniques. As shown, in FIG. 19, prediction unit 32 ofvideo encoder 50 defines eight sets of filter coefficients for fifteensub-pixel positions based on coefficient symmetry and pixel symmetry, asdescribed herein (201). In particular, prediction unit 32 may use pixelsymmetry to reduce the sets of coefficients from fifteen to eight, andmay further reduce the number of coefficients for some or all of theeight sets based on coefficient symmetry between coefficients of givensets. Prediction unit 32 can then encode video data using interpolationfilters (such as filter(s) 39) and the eight sets of filter coefficients(202). Video encoder 50 may output encoded video data and the eight setsof filter coefficients (203). The eight sets of coefficients, which areencoded and output in the bitstream, may be sent with each coded unit(e.g., each frame or slice) so that a decoding device can be informed ofthe filter coefficients to use for interpolation. Alternatively,different groups of eight sets of coefficients may be encoded and sentwith each coded unit to enable different types of interpolations atdifferent locations within a coded unit.

FIG. 20 is a flow diagram illustrating example operation of a videodecoder that utilizes coefficient symmetry and pixel symmetry consistentwith this disclosure. In this case, prediction unit 55 of video decoder60 receives eight sets of filter coefficients (191), and generates thefifteen sets of filter coefficients based on the eight sets, coefficientsymmetry and pixel symmetry. Prediction unit 60 can then program itsinterpolation filters and apply such interpolation filters in order toproperly generate interpolated predictive data (193). Video decoder 60can then decode video blocks based on the predictive data (194), e.g.,invoking adder 64 to add correct predictive data interpolated byprediction unit 55 to a residual block to be decoded.

FIG. 21 is a flow diagram illustrating example operation of a videoencoder that utilizes filtering of integer pixel locations to generateadjusted integer pixel values consistent with this disclosure. This isdiscussed above as interpolation-like filtering insofar as it is similarto interpolations, but does not generate sub-integer values. Rather,this process generates new integer values that are filtered based on theoriginal integer value and other integer values that surround theoriginal integer value.

FIG. 21 will be described from the perspective of video encoder 50 ofFIG. 2 although other devices may perform similar techniques. As shown,in FIG. 21, MC unit 37 of prediction unit 32 obtains a block of pixelsfrom memory 34 that includes integer pixel values corresponding tointeger pixel locations (211). Filter(s) 39 of prediction unit 32 filterthe integer pixel values based on other integer pixel values of theblock of pixels to generate adjusted integer pixel values (212).Prediction unit 32 generates a prediction block based on the adjustedinteger pixel values (213), and video encoder 50 encodes a video blockbased on the prediction block (214), e.g., invoking adder 48 to subtractthe prediction block from the video block being encoded to generate aresidual block. Following, transformation and quantization of theresidual block by transform unit 38 an quantization unit 40, andfollowing entropy coding by entropy coding unit 46, video encoder 50outputs the encoded video block and filter information (215). As withsub-pel interpolation, interpolation-like filtering with respect tointeger pixel locations may involve the output and communication offilter information once per coded unit, or possibly several times percoded unit if different areas of a coded unit use different types ofinteger interpolation-like filtering.

FIG. 22 is a flow diagram illustrating example operation of a videodecoder that utilizes filtering of integer pixel locations to generateadjusted integer pixel values consistent with this disclosure.Prediction unit 55 of video decoder 60 receives encoded video blocks,and also receives filter information (e.g., once per coded unit) (221).Prediction unit 55 obtains a block of pixels from memory 62 that includeinteger pixel values corresponding to integer pixel positions (222).Prediction unit 55 invokes a filter (not shown) as part of motioncompensation to filter integer pixel values based on other integer pixelvalues of the block of pixels to generate adjusted integer pixel values(223). Prediction unit 55 generates a prediction block based on theadjusted integer pixel values (224). Video decoder 60 can then decodevideo blocks based on the prediction block e.g., invoking adder 64 toadd the predictive block interpolated by prediction unit 55 to aresidual video block to be decoded (225).

FIG. 23 is a flow diagram illustrating a technique for rate-distortiondefined interpolation for video coding based on two sets of candidatefilters. In this case, MC unit 37 of prediction unit 32 may generate twoor more different sets of predictive data, one set based on the firstset of candidate interpolation filters and another set based on thesecond set of candidate interpolation filters. MC unit 37 can then use arate-distortion based analysis in order to select which interpolationfilter will yield the best results from the perspective of rate anddistortion. In this way, MC unit 37 not only considers which set ofpredictive data will yield the best results (i.e., the leastdistortion), but will also factor in the fact that one set of candidatefilters may require less bit overhead in order to convey its filtercoefficients to the decoder.

As shown in FIG. 23, MC unit 37 of prediction unit 32 generates firstinterpolated predictive data for encoding of video data based on a firstinterpolation filter (231). The first interpolation filter, in oneexample, comprises a fixed interpolation filter corresponding to afilter defined by a particular video coding standard, such as a filterthat corresponds to an ITU-T H.264 interpolation filter. For addedprecision, in some cases, the fixed interpolation filter may correspondto an ITU-T H.264 interpolation filter without intermediate rounding ofhalf-pixel values that are used to generate quarter-pixel values. Inthis case, the fixed interpolation filter may generate half-pixel valuesand may upwardly round such values for purposes of interpolation to thehalf-pixel resolution. However, to the extent that such half-pixelvalues will also be used for interpolation to quarter-pixel resolution,MC unit 37 may store non-rounded versions of the half-pixel values anduse the non-rounded versions of the half-pixel values for anyinterpolation to quarter-pixel resolution. Co-pending and commonlyassigned U.S. patent application Ser. No. 12/420,235, filed forKarczewicz et al., and entitled “ADVANCED INTERPOLATION TECHNIQUES FORMOTION COMPENSATION IN VIDEO CODING”, which was filed on the same day asthe present application, and which is incorporated herein by reference,provides many additional details on interpolation filtering withoutintermediate rounding of half-pixel values that are used to generatequarter-pixel values.

Next, MC unit 37 of prediction unit 32 generates second interpolatedpredictive data for video encoding of the video data based on a secondinterpolation filter (232). In one example, the second interpolationfilter may comprise an adaptive interpolation filter. In this case,consistent with adaptive interpolation, MC unit 37 may define the filtercoefficients to be used. In particular, MC unit 37 may execute anadaptive interpolation process in which MC unit 37 starts with pre-setfilter coefficients, generates preliminary predictive data, and thenadjusts the filter coefficients in an adaptive process so that suchfilter coefficients define more desirable predictive data.

Once the MC unit 37 of prediction unit 32 has generated both the firstand the second interpolated predictive data, MC unit 37 can selectbetween the first interpolated predictive data and the secondinterpolated predictive data based on a rate-distortion analysis (233).In this way, MC unit 37 not only considers which set of predictive datawill yield the best results (i.e., the least distortion), but MC unit 37also factors in the different amounts of data (i.e., the different bitrates) that will be required for the first interpolation filter relativeto the second interpolation filter. Notably, if the fixed interpolationfilter is used (e.g., as the first interpolation filter), video encoder50 will not need to encode filter coefficients in the bitstream, whereasif the adaptive interpolation filter is used (e.g., as the secondinterpolation filter) video encoder 50 will need to encode filtercoefficients. Accordingly, the rate-distortion analysis can determinewhich set of predictive data will yield the best results (i.e., theleast rate distortion cost), by accounting for the fact that the use ofthe fixed interpolation filter does not require additional bits in orderto convey filter coefficients to the decoder.

More specifically, selecting between the first interpolated predictivedata and the second interpolated predictive data based on arate-distortion analysis (233) may comprise calculating a firstrate-distortion cost associated with the video data if the video data isencoded via the first interpolated predictive data, calculating a secondrate-distortion cost associated with the video data if the video data isencoded via the second interpolated predictive data, and selectingbetween the first interpolated predictive data and the secondinterpolated predictive data based on the first and secondrate-distortion costs.

The rate distortion cost associated with the video data if the videodata is encoded via the first interpolated predictive data may comprisea first difference metric, e.g., mean squared error (MSE) of pixelvalues or sum of absolute difference (SAD) of pixel values or sum ofsquared difference (SSD) of pixel values indicative of differencesbetween the video data and the first interpolated predictive data, plusa first value that quantifies cost associated with encoding of filtercoefficients. In this case, if the first interpolation filter is a fixedinterpolation filter, the first value that quantifies cost may bedefined as zero. Similarly, the second rate-distortion cost may comprisea second difference metric indicative of differences between the videodata and the second interpolated predictive data (MSE, SAD or SSD), plusa second value that quantifies cost associated with encoding of filtercoefficients. In the case where the second interpolation filter is anadaptive interpolation filter, the second value that quantifies costassociated with encoding of filter coefficients may comprise the numberof bits (r) needed to encode adaptive interpolation filter coefficients,or possibly this number (r) multiplied by a Lagrangian multiplier (λ).

After selecting between the first interpolated predictive data and thesecond interpolated predictive data based on a rate-distortion analysis(233), MC unit 37 of prediction unit 32 encodes the video data based onthe selection (234), and encodes syntax to indicate the selection (235).The syntax may comprise a one-bit flag or a multi-bit flag that defineswhether the first interpolation filter or the second interpolationfilter should be used by a decoder. The process of FIG. 23 may berepeated for each of a plurality of sub-pixel locations for each codedunit of a video sequence to indicate whether the first interpolationfilter or the second interpolation filter should be used by a decoderfor each of the plurality of sub-pixel locations. The sub-pixellocations may comprise fifteen possible sub-pixel locations consistentwith interpolation to quarter-pixel resolution, or may comprise adifferent number of sub-pixel locations. The process of FIG. 23 may alsobe repeated for the interger-pixel location for each coded unit of avideo sequence to indicate whether the first interpolation filter or thesecond interpolation filter should be used by a decoder for theinteger-pixel location.

Once video encoder 50 (FIG. 2) has encoded the video data based on theselection (234), and encoded syntax to indicate the selection (235),modem 23 and transmitter 24 (FIG. 1) may modulate and transmit theencoded video data and the syntax to destination device 17. In the casewhere the first interpolation filter is fixed and the secondinterpolation filter is adaptive, transmitter 24 may transmit filtercoefficients when the syntax indicates that the second interpolatedpredictive data was used to generate the encoded video data, but maytransmit no filter coefficients when the syntax indicates that the firstinterpolated predictive data was used to generate the encoded videodata. In this way, when the first interpolation filter is fixed and thesecond interpolation filter is adaptive, filter coefficients are onlysent if the syntax indicates that adaptive interpolation filtering wasused, and the decision whether or not to use adaptive interpolationfiltering considers not only the predictive video quality, but also thebit rate, which is affected by the presence of filter coefficients inthe transmitted bitstream. In other examples, however, the first andsecond interpolation filters may both be fixed, or may both be adaptive.

The techniques of this disclosure may be applied in many scenarios,including scenarios when there are more than two sets of filters beingselected by the encoder. In other words, additional interpolationfilters may also be applied to generate additional interpolatedpredictive data, which may also be considered in the rate distortionanalysis. In other words, the method is not limited to generating onlyfirst and second interpolated predictive data based on two interpolationfilters, but could be applied to generate any plurality of interpolatedpredictive data based on any number of interpolation filters.Importantly, a rate-distortion analysis interpolated predictive data isused to identify which filter to select. In one example, a methodexecuted by video encoder 50 may comprise generating a plurality ofdifferent versions of predictive data for encoding of video data basedon a plurality of different interpolation filters, selecting among theplurality of different versions of predictive data based on a ratedistortion analysis, encoding the video data based on the selection, andencoding syntax to indicate the selection.

In addition to selecting the interpolation filter based on arate-distortion analysis, and generating syntax to indicate theselection, MC unit 37 of prediction unit 32 may also conduct a similarrate-distortion analysis with respect to integer pixel filtering, whichis explained in more detail above. In particular, MC unit 37 ofprediction unit 32 may generate two or more different sets of predictivedata for integer pixel locations, e.g., one with integer pixel filteringand one without integer pixel filtering, and may conduct arate-distortion analysis with respect to these two different sets inorder to determine whether integer pixel filtering is desired.Accordingly, MC unit 37 of prediction unit 32 may generate anothersyntax element based on a rate-distortion analysis associated with twodifferent sets of integer predictive data to indicate whether integerpixel filtering should be applied for the coded unit, wherein a firstset of the integer predictive data is non-filtered and a second set ofthe integer predictive data is filtered. In this way, the decisionwhether to conduct integer pixel filtering may be based on not only thequality of video coding, but also the possible bit overhead associatedwith integer pixel filtering, particularly when integer pixel filteringwould involve the encoding and sending of integer pixel filtercoefficients used to perform such integer pixel filtering. Integerfiltering may also consider N integer filters (e.g., where N is anypositive and plural integer. Consistent with the use of N integerfilters, the example above would correspond to the case where N is twoand one of the filters does not apply any filtering.

Whenever interpolation filter coefficients are actually encoded and sentfrom source device 12 to destination device 16, this disclosure alsocontemplates techniques for coding such interpolation filtercoefficients. The described encoding for filter coefficients can improvedata compression. In particular, this disclosure contemplates predictiontechniques for filter coefficients, e.g., relative to fixed filtercoefficients. In addition, this disclosure contemplates predictiontechniques for a second set of filter coefficients relative to first setof filter coefficients. In these ways, imperfect symmetry betweendifferent filter coefficients may be exploited to allow for datacompression. In addition to the use of such prediction techniques forinterpolation filter coefficients, this disclosure also provides foruseful quantization techniques and entropy coding of interpolationfilter coefficients based on prefix and suffix coding. The followingdiscussion provides more details on these aspects of this disclosure.

FIG. 24 is a flow diagram illustrating a technique for encoding filtercoefficients using predictive coding. In this case, filter coefficientsused by video encoder 50 may be predictively encoded relative to filtercoefficients of a fixed filter, which may further improve datacompression when filter coefficients are sent as part of an encodedbitstream.

As shown in FIG. 24, MC unit 37 of prediction unit 32 identifies a setof filter coefficients for interpolation of predictive data in videoencoding (241). For example, MC unit 37 may identify the set of filtercoefficients by performing an adaptive interpolation filtering process.In this case, MC unit 37 may determine the set of filter coefficientsvia the adaptive interpolation filtering process and generate thepredictive data based on the filter coefficients identified in theadaptive interpolation filtering process. In adaptive interpolationfiltering, as explained in greater detail herein, MC unit 37 may performa two-pass approach in which predictive data is generated based on afixed filter, and then the filter coefficients are adjusted so that thepredictive data is made to be more similar to the video data beingcoded. The adjusted filter coefficients, then, define the filtercoefficients that are used and encoded into the bitstream.

In order to encode the filter coefficients so that such filtercoefficients may be transmitted as part of a bitstream, coefficientprediction and quantization unit 41 may generate residual valuesassociated with the set of filter coefficients based on predictivecoding of the set of filter coefficients relative to filter coefficientsassociated with a fixed interpolation filter (242). In particular,coefficient prediction and quantization unit 41 may subtract the filtercoefficients determined in the adaptive interpolation filtering processfrom corresponding filter coefficients associated with a fixed filter,such as an ITU-T H.264 interpolation filter, or an ITU-T H.264interpolation filter without intermediate rounding of half-pixel values.By encoding and transmitting residual values, rather than transmittingthe actual filter coefficients, the amount of data communicated in thebitstream can be reduced. In this case, the decoder may be programmed toknow the manner in which the filter coefficients are encoded.

Video encoder 50 may invoke coefficient prediction and quantization unit41 to both predict and quantize the residual coefficients, and entropycoding unit 46 may entropy code the quantized residuals (243). Videoencoder 50 can then output the residual values as part of the encodedbitstream (244). The quantization of residual values associated with thefilter coefficients may involve quantizing the residual values, whereinat least some of the residual values associated with different filtercoefficients are assigned different levels of quantization. In this way,coefficient prediction and quantization unit 41 may assign morequantization to larger residual coefficients, and may assign lessquantization to finer residual coefficients in order to achieve adesirable balance of quantization and accuracy. When more quantizationis used, more data is eliminated but more compression may be achieved.Entropy coding unit 46 may entropy code the residual values by assigninga prefix code, assigning a suffix code and assigning a sign value forthe residual values following quantization of the residual values. Videoencoder 50 can then output the residual values as part of the encodedbitstream (244).

FIG. 27 is a conceptual graph illustrating some illustrative filtercoefficients that can be predictively coded. In this case, filtercoefficients O₁, O₂, O₃ and O₄ may define filter coefficients associatedwith a fixed interpolation filter. Filter coefficients X₁, X₂, X₃ and X₄may define the desired filter coefficients, which may be fairly similarto those of the fixed interpolation filter. Accordingly, filtercoefficients X₁, X₂, X₃ and X₄ may be predicted based on filtercoefficients O₁, O₂, O₃ and O₄ respectively. In particular, a firstresidual may be formed as the difference between X₁ and O₁. Similarly, asecond residual may be formed as the difference between X₂ and O₂, athird residual may be formed as the difference between X₃ and O₃, and afourth residual may be formed as the difference between X₄ and O₄. Theresiduals may comprise less data than the original filter coefficients,thereby promoting data compression.

In some cases, the set of filter coefficients comprises a first set offilter coefficients that define only a portion of an entire set offilter coefficients associated with an adaptive interpolation filter,wherein a second set of filter coefficients can be determined by adecoder based on the first set of filter coefficients and coefficientsymmetry. For example, filter coefficients X₁ and X₂ may be predictivelyencoded based on O₁ and O₂ respectively. In this case, however, X₃ andX₄ may be symmetric with X₁ and X₂ and the decoder may be programmed toknow that such symmetry is imposed. Accordingly, through the use ofsymmetry, coefficients X₃ and X₄ in this simple example, may beeliminated from the bitstream, and may be calculated at the decoderbased on known coefficient symmetry once coefficients X₁ and X₂ arepredictively decoded.

FIG. 25 is another flow diagram illustrating a technique for encodingfilter coefficients using predictive coding. In this case, however, twodifferent types of prediction are used. As shown in FIG. 25, MC unit 37of prediction unit 32 identifies a set of filter coefficients forinterpolation of predictive data in video encoding (251). As with theprocess of FIG. 24, in FIG. 25, MC unit 37 may identify the set offilter coefficients by performing an adaptive interpolation filteringprocess. In this case, MC unit 37 may determine the set of filtercoefficients via the adaptive interpolation filtering process andgenerate the predictive data based on the filter coefficients identifiedin the adaptive interpolation filtering process. In adaptiveinterpolation filtering, MC unit 37 may perform a two-pass approach inwhich predictive data is generated based on a fixed filter, and then thefilter coefficients are adjusted so that the predictive data is made tobe more similar to the video data being coded. The adjusted filtercoefficients, then, define the filter coefficients that are used andencoded into the bitstream.

In order to encode the filter coefficients so that such filtercoefficients may be transmitted as part of a bitstream, coefficientprediction and quantization unit 41 may generate a first set of residualvalues associated with a first set of the filter coefficients based onpredictive coding relative to filter coefficients associated with afixed interpolation filter (252). In particular, coefficient predictionand quantization unit 41 may subtract the first set of filtercoefficients from corresponding filter coefficients associated with afixed filter, such as an ITU-T H.264 interpolation filter, or an ITU-TH.264 interpolation filter without intermediate rounding of half-pixelvalues. Next, coefficient prediction and quantization unit 41 maygenerate a second set of residual values associated with a second set ofthe filter coefficients based on predictive coding relative to the firstset of filter coefficients (253). In particular, coefficient predictionand quantization unit 41 may subtract the second set of filtercoefficients from mirrored or rotated values of the first set ofcoefficients. Thus, the first set of coefficients are predictively codedbased on the coefficients of a fixed filter, and the second set ofcoefficients are predictively coded based on the first set ofcoefficients. By generating residual values rather than using the actualfilter coefficients, the amount of data communicated in the bitstreamcan be reduced. Furthermore, by using the fixed filter to predict thefirst set of coefficients and then using the first set of coefficientsto predict the second set of coefficients, further data compression maybe achieved relative to prediction that relies only on the fixed filter.

As with the process of FIG. 24, in FIG. 25, video encoder 50 may invokecoefficient prediction and quantization unit 41 to both predictivelycode and quantize the residual coefficients, and entropy coding unit 46may entropy code the quantized residuals (254). Video encoder 50 canthen output the residual values as part of the encoded bitstream (254).Again, the quantization of residual values associated with the filtercoefficients may involve quantizing the residual values, wherein atleast some of the residual values associated with different filtercoefficients are assigned different levels of quantization. In this way,coefficient prediction and quantization unit 41 may assign morequantization to larger residual coefficients, and may assign lessquantization to finer residual coefficients in order to achieve adesirable balance of quantization and accuracy. Entropy coding unit 46may entropy code the residual values by assigning a prefix code,assigning a suffix code and assigning a sign value for the residualvalues following quantization of the residual values. Video encoder 50can then output the residual values as part of the encoded bitstream(255).

FIG. 28 is a conceptual graph illustrating some illustrative filtercoefficients that can be predictively coded consistent with the processof FIG. 25. In this case, filter coefficients O₁ and O₂ may definefilter coefficients associated with a fixed interpolation filter. Filtercoefficients X₁ and X₂ may define a first set of the desired filtercoefficients, which may be fairly similar to those of the fixedinterpolation filter. Accordingly, filter coefficients X₁ and X₂ may bepredicted based on filter coefficients O₁ and O₂ respectively. Inparticular, a first residual may be formed as the difference between X₁and O₁, and a second residual may be formed as the difference between X₂and O₂. The residuals may comprise less data than the original filtercoefficients, thereby promoting data compression. The residuals are thenquantized by coefficient prediction and quantization unit 41 and entropycoded by entropy coding unit 46. {circumflex over (X)}₁ and {circumflexover (X)}₂ may refer to modified filter coefficients that are generatedby adding the dequantized residuals to the prediction filtercoefficients O₁ and O₂.

Next, a second set of filter coefficients Z₁ and Z₂ may be predictivelycoded based on the first set of coefficients X₁ and X₂, e.g.,specifically from coefficients {circumflex over (X)}₁ and {circumflexover (X)}₂ which are defined based on coefficients X₁ and X₂. Inparticular, a third residual may be formed as the difference between Z₁and {circumflex over (X)}₁, and a fourth residual may be formed as thedifference between Z₂ and {circumflex over (X)}₂. {circumflex over (X)}₁and {circumflex over (X)}₂ may be more similar to Z₁ and Z₂ than O₁ andO₂ and therefore, by using {circumflex over (X)}₁ and {circumflex over(X)}₂ to predictively encode Z₁ and Z₂ further data compression may bepromoted.

FIG. 26 is a flow diagram illustrating a technique for decoding filtercoefficients using predictive coding. FIG. 26 will be described from theperspective of video decoder 60 of FIG. 16. As shown video decoder 60receives residual values associated with a set of filter coefficients(261). Video decoder 60 may entropy decode the residual values viaentropy decoding unit 52, and may invoke coefficient prediction andinverse quantization unit 53 to de-quantize the residual values (262),which are then sent to prediction unit 55. Prediction unit 56 generatesthe set of filter coefficients using predictive decoding of the residualvalues (263).

In particular, prediction unit 56 may generate the entire set of filtercoefficients based on the residual values and filter coefficientsassociated with a fixed interpolation filter, e.g., as conceptuallyillustrated in FIG. 27 and addressed above in the context of encoding.In some cases, a first set of filter coefficients may be generated basedon the residual values and filter coefficients associated with a fixedinterpolation filter, and a second set of filter coefficients may begenerated based on symmetry. In other cases, a first set of filtercoefficients may be generated based on the residual values and filtercoefficients associated with a fixed interpolation filter, and a secondset of filter coefficients may be generated based on additional residualvalues and the first set of filter coefficients, e.g., as conceptuallyillustrated in FIG. 28 and addressed above in the context of encoding.In any case, prediction unit 56 of video decoder 60 applies the set offilter coefficients to interpolate predictive data used for predictivedecoding of video blocks (264). In particular, prediction unit 56filters data to generate interpolated predictive data using thepredictively decoded filter coefficients so that video blocks can bedecoded based on such interpolated predictive data.

Again, the set of predictively decoded filter coefficients may comprisea first set of filter coefficients that define only a portion of anentire set of filter coefficients associated with an adaptiveinterpolation filter. In this case, coefficient prediction and inversequantization unit 53 of video decoder 60 may generate a second set offilter coefficients based on the first set of filter coefficients andcoefficient symmetry, and apply the first and second sets of filtercoefficients to interpolate the predictive data.

In another case, the set of predictively decoded filter coefficients maycomprises a first set of filter coefficients that define only a portionof an entire set of filter coefficients associated with an adaptiveinterpolation filter. In this case, video decoder 60 may receiveadditional residual values associated with the entire set of filtercoefficients. Coefficient prediction and inverse quantization unit 53may generate a second set of filter coefficients using predictivedecoding based on additional residual values and the first set of filtercoefficients, and prediction unit 55 may apply the first and second setsof filter coefficients to interpolate the predictive data.

In some cases, fixed interpolation filters based on H.264/AVC filtersmay be used to predict the 1-D filters (which may include filters forthe sub-pixel positions a, b, d, h shown in FIGS. 8, 9, 10 and 11). Forthe 2-D filters, which include filters for the sub-pixel positions e, f,i, and j shown in FIGS. 12, 13, 14 and 15, one of the followingprediction schemes may be used:

-   -   1. Set prediction to zero (no prediction), p(h_(i,j) ^(SP))=0,        SPε{e,f,i,j}    -   2. Use a fixed filter prediction, such as the average filter        gathered over a training set, i.e., p(h_(i,j) ^(SP))= h _(i,j)        ^(SP), SPε{e,f,i,j}, where h _(i,j) ^(SP) is the (i,j)-th filter        coefficient in the average filter for sub-pixel position SP.    -   3. Exploit the possible symmetry in the coefficients and use the        already coded coefficients to predict the remaining        coefficients.        For the 1-D filters, any of these three prediction methods may        also be applied.

For prediction, FIG. 29 provides an example of an array of integer-pixelfilter coefficients for which prediction techniques may be used forencoding. In this example, it may be assumed that no symmetry is imposedon the integer-pixel filter. Coefficients (h0,0), (h0,1), (h1,0),(h1,1), (h2,0), (h2,1) (h0,2), (h1,2) and (h2,2) may be quantized andcoded first. Then, the already coded top-left coefficients (h0,0),(h0,1), (h1,0), (h1,1) and (h2,0) may be used to predict the top-rightcoefficients (h0,3), (h1,3), (h2,3), (h0,4), (h1,4), and (h2,4). Next,once the top half of the filter coefficients (h0,0), (h0,1), (h0,2),(h0,3) (h0,4), (h1,0), (h1,1), (h1,2), (h1,3) and (h1,4) are quantizedand coded, they may be further used to predict the bottom half of thefilter coefficients (h3,0), (h3,1), (h3,2), (h3,3), (h3,4), (h4,0),(h4,1), (h4,2), (h4,3) and (h4,4). Prediction of filter coefficients maybe done in a similar fashion. For example, for the sub-pixel position“e” filter (see FIG. 12), which may have some symmetry diagonally, thetop-right coefficients may be quantized and coded first, and then usedto predict the bottom-left coefficients.

In any case, after prediction of the coefficients (e.g., by coefficientprediction and quantization unit 41 of prediction unit 32), predictionerrors are quantized (e.g., by coefficient prediction and quantizationunit 41). As outlined above, so-called “uneven quantization” may beused. In this case, the quantization precision applied by coefficientprediction and quantization unit 41 may depend on the coefficientlocation. It has been found that for coefficients with smaller magnitude(which are typically coefficients farther away from the center of thefilter), better precision may be desirable. In contrast, coefficientswith larger magnitude (which are typically coefficients closer to thecenter of the filter), less precision is more desirable.

The following matrices, Q^(1D), Q^(2D), Q^(FP), may be used bycoefficient prediction and quantization unit 41 to specify thequantization precision for coefficients in the 1D filters, the 2Dfilters, and the integer-pixel filter, respectively. Note that thenumbers of bits given in the matrices may include 1 bit to encode thesign of the respective coefficients.

Q^(1D) = [12  11  9  9  11  12] $Q^{2D} = \begin{bmatrix}0 & 10 & 10 & 0 \\10 & 9 & 9 & 10 \\10 & 9 & 9 & 10 \\0 & 10 & 10 & 0\end{bmatrix}$ $Q^{FP} = \begin{bmatrix}11 & 11 & 11 & 11 & 11 \\11 & 10 & 10 & 10 & 11 \\11 & 10 & 9 & 10 & 11 \\11 & 10 & 10 & 10 & 11 \\11 & 11 & 11 & 11 & 11\end{bmatrix}$

Coefficient prediction and quantization unit 41 may code the quantizedcoefficient prediction errors i.e., the coefficient residuals, based ona simple prefix coding scheme. First, the dynamic range of the magnitudeof the prediction error is divided into N bins, for example N=6. If thedynamic range of the magnitude is [0, . . . ,2^(q-1)−1], where q is thequantization precision for the given coefficient position, (such asspecified in the matrices above) then each bin n, n=0, . . . N−1, mayspan the following ranges:

$\begin{matrix}{\left\lbrack {n_{start},n_{end}} \right) = \left\lbrack {0,\ldots\mspace{14mu},2^{q - N}} \right)} \\{{\left\lbrack {n_{start},n_{end}} \right) = \left\lbrack {2^{q - N + n - 1},2^{q - N + n}} \right)},}\end{matrix}$ if $\begin{matrix}{n = 0} \\{n > 0}\end{matrix}$

In this case, the bin b, b=0, . . . , N−1, to which the input magnitudem belongs, may be coded using a unary prefix code (which takes b+1bits). Then, the remainder of the magnitude, m−b_(start), may be codedusing fixed length suffix code of (q−N+b−1) bits. Finally, the sign ofthe prediction error is coded using 1 bit.

For example, for the center coefficient in the 1D filter, 9 bitsprecision may be used by coefficient prediction and quantization unit 41to quantize the prediction error, i.e., q=9, of which 8 bits may be usedto quantize the error magnitude. If the error magnitude is 97, then itfalls within the 4th bin, i.e., b=4. Therefore, the unary prefix code“11110” may be encoded to indicate the 4th bin, and the remainder ofm−b_(start)=97−64=33 may be encoded using a fixed length suffix code of(q−N+b−1)=(9−6+4−1)=6 bits. Finally, a bit “0” can be encoded toindicate positive sign.

The techniques of this disclosure may be implemented in a wide varietyof devices or apparatuses, including a wireless handset, and integratedcircuit (IC) or a set of ICs (i.e., a chip set). Any components, modulesor units have been described provided to emphasize functional aspectsand does not necessarily require realization by different hardwareunits.

Accordingly, the techniques described herein may be implemented inhardware, software, firmware, or any combination thereof. If implementedin hardware, any features described as modules, units or components maybe implemented together in an integrated logic device or separately asdiscrete but interoperable logic devices. If implemented in software,the techniques may be realized at least in part by a computer-readablemedium comprising instructions that, when executed in a processor,performs one or more of the methods described above. Thecomputer-readable medium may comprise a computer-readable storage mediumand may form part of a computer program product, which may includepackaging materials. The computer-readable storage medium may compriserandom access memory (RAM) such as synchronous dynamic random accessmemory (SDRAM), read-only memory (ROM), non-volatile random accessmemory (NVRAM), electrically erasable programmable read-only memory(EEPROM), FLASH memory, magnetic or optical data storage media, and thelike. The techniques additionally, or alternatively, may be realized atleast in part by a computer-readable communication medium that carriesor communicates code in the form of instructions or data structures andthat can be accessed, read, and/or executed by a computer.

The code may be executed by one or more processors, such as one or moredigital signal processors (DSPs), general purpose microprocessors, anapplication specific integrated circuits (ASICs), field programmablelogic arrays (FPGAs), or other equivalent integrated or discrete logiccircuitry. Accordingly, the term “processor,” as used herein may referto any of the foregoing structure or any other structure suitable forimplementation of the techniques described herein. In addition, in someaspects, the functionality described herein may be provided withindedicated software modules or hardware modules configured for encodingand decoding, or incorporated in a combined video codec. Also, thetechniques could be fully implemented in one or more circuits or logicelements.

Various aspects of the disclosure have been described. These and otheraspects are within the scope of the following claims.

The invention claimed is:
 1. A method comprising: identifying a set offilter coefficients associated with a filter for interpolation ofpredictive data in video encoding, wherein the set of filtercoefficients comprises a first set of filter coefficients associatedwith the filter and forming a first portion of the set of filtercoefficients and a second set of filter coefficients associated with thefilter and forming a second portion of the set of filter coefficients;generating residual values associated with the first set of filtercoefficients based on predictive coding of the first set of filtercoefficients relative to filter coefficients associated with a fixedinterpolation filter; generating additional residual values associatedwith the second set of filter coefficients relative to one or moremirrored or rotated values of the first set of filter coefficients;applying quantization to the residual values and the additional residualvalues; and entropy coding and outputting the quantized residual valuesand the quantized additional residual values as part of an encodedbitstream.
 2. The method of claim 1, wherein identifying the set offilter coefficients includes performing an adaptive interpolationfiltering process to determine the set of filter coefficients andgenerate the predictive data.
 3. The method of claim 1, wherein at leastsome of the residual values associated with different filtercoefficients are assigned different levels of quantization.
 4. Themethod of claim 3, wherein at least some of the residual values withsmaller magnitude are assigned finer quantization and at least some ofthe residual values with larger magnitude are assigned coarserquantization.
 5. The method of claim 3, further comprising entropycoding the quantized residual values and the quantized additionalresidual values prior to outputting the quantized residual values andthe quantized additional residual values, wherein entropy coding thequantized residual values and the quantized additional residual valuescomprises assigning a prefix code, assigning a suffix code and assigninga sign value for the quantized residual values and the quantizedadditional residual values following quantization of the residual valuesand the additional residual values.
 6. A method comprising: receivingresidual values and additional residual values associated with a set offilter coefficients associated with a filter, wherein the set of filtercoefficients includes a first set of filter coefficients associated withthe filter and forming a first portion of the set of filter coefficientsand a second set of filter coefficients associated with the filter andforming a second portion of the set of filter coefficients; generatingthe first set of filter coefficients using predictive decoding based onthe residual values and filter coefficients associated with a fixedinterpolation filter; generating the second set of filter coefficientsusing predictive decoding based on the additional residual values andthe first set of filter coefficients, wherein the additional residualvalues are relative to one or more mirrored or rotated values of thefirst set of filter coefficients; and applying the set of filtercoefficients to interpolate predictive data used for predictive decodingof video blocks.
 7. The method of claim 6, further comprising entropydecoding and de-quantizing the received residual values and theadditional residual values prior to generating the first and second setsof filter coefficients and applying the set of filter coefficients. 8.An apparatus comprising a video encoder that: identifies a set of filtercoefficients associated with a filter for interpolation of predictivedata in video encoding, wherein the set of filter coefficients comprisesa first set of filter coefficients associated with the filter andforming a first portion of the set of filter coefficients and a secondset of filter coefficients associated with the filter and forming asecond portion of the set of filter coefficients; generates residualvalues associated with the first set of filter coefficients based onpredictive coding of the first set of filter coefficients relative tofilter coefficients associated with a fixed interpolation filter;generates additional residual values associated with the second set offilter coefficients relative to one or more mirrored or rotated valuesof the first set of filter coefficients; applies quantization to theresidual values and the additional residual values; and entropy codesand outputs the quantized residual values and the quantized additionalresidual values as part of an encoded bitstream.
 9. The apparatus ofclaim 8, wherein in identifying the set of filter coefficients, thevideo encoder performs an adaptive interpolation filtering process todetermine the set of filter coefficients and generate the predictivedata.
 10. The apparatus of claim 8, wherein at least some of theresidual values associated with different filter coefficients areassigned different levels of quantization.
 11. The apparatus of claim10, wherein at least some of the residual values with smaller magnitudeare assigned finer quantization and at least some of the residual valueswith larger magnitude are assigned coarser quantization.
 12. Theapparatus of claim 10, wherein the video encoder entropy encodes thequantized residual values and the quantized additional residual valuesprior to outputting the quantized residual values, wherein entropyencoding the quantized residual values and the quantized additionalresidual values comprises assigning a prefix code, assigning a suffixcode and assigning a sign value for the quantized residual values andthe quantized additional residual values following quantization of theresidual values and the additional residual values.
 13. The apparatus ofclaim 8, wherein the video encoder comprises an integrated circuit. 14.The apparatus of claim 8, wherein the video encoder comprises amicroprocessor.
 15. The apparatus of claim 8, wherein the apparatuscomprises a wireless communication device that includes the videoencoder.
 16. An apparatus comprising a video decoder that: receivesresidual values and additional residual values associated with a set offilter coefficients associated with a filter, wherein the set of filtercoefficients includes a first set of filter coefficients associated withthe filter and forming a first portion of the set of filter coefficientsand a second set of filter coefficients forming a second portion of theset of filter coefficients; generates the first set of filtercoefficients using predictive decoding based on the first set ofresidual values and filter coefficients associated with a fixedinterpolation filter; generates the second set of filter coefficientsusing predictive decoding based on the additional residual values andthe first set of filter coefficients, wherein the additional residualvalues are relative to one or more mirrored or rotated values of thefirst set of filter coefficients; and applies the set of filtercoefficients to interpolate predictive data used for predictive decodingof video blocks.
 17. The apparatus of claim 16, wherein the videodecoder entropy decodes and de-quantizes the received residual valuesand the additional residual values prior to generating the first andsecond sets of filter coefficients and applying the set of filtercoefficients.
 18. The apparatus of claim 16, wherein the video decodercomprises an integrated circuit.
 19. The apparatus of claim 16, whereinthe video decoder comprises a microprocessor.
 20. The apparatus of claim16, wherein the apparatus comprises a wireless communication device thatincludes the video decoder.
 21. A device comprising: means foridentifying a set of filter coefficients associated with a filter forinterpolation of predictive data in video encoding, wherein the set offilter coefficients comprises a first set of filter coefficientsassociated with the filter and forming a first portion of the set offilter coefficients and a second set of filter coefficients associatedwith the filter and forming a second portion of the set of filtercoefficients; means for generating residual values associated with thefirst set of filter coefficients based on predictive coding of the firstset of filter coefficients relative to filter coefficients associatedwith a fixed interpolation filter; means for generating additionalresidual values associated with the second set of filter coefficientsrelative to one or more mirrored or rotated values of the first set offilter coefficients; means for applying quantization to the residualvalues and the additional residual values; and means for entropy codingand outputting the quantized residual values and the quantizedadditional residual values as part of an encoded bitstream.
 22. Thedevice of claim 21, wherein means for identifying the set of filtercoefficients includes means for performing an adaptive interpolationfiltering process to determine the set of filter coefficients andgenerate the predictive data.
 23. The device of claim 21, wherein atleast some of the residual values associated with different filtercoefficients are assigned different levels of quantization.
 24. Themethod of claim 23, wherein at least some of the residual values withsmaller magnitude are assigned finer quantization and at least some ofthe residual values with larger magnitude are assigned coarserquantization.
 25. The device of claim 23, further comprising means forentropy coding the quantized residual values and the quantizedadditional residual values prior to outputting the quantized residualvalues and the quantized additional residual values, wherein means forentropy coding the quantized residual values and the quantizedadditional residual values comprises means for assigning a prefix code,means for assigning a suffix code and means for assigning a sign valuefor the quantized residual values and the quantized additional residualvalues following quantization of the residual values and the additionalresidual values.
 26. A device comprising: means for receiving residualvalues and additional residual values associated with a set of filtercoefficients associated with a filter, wherein the set of filtercoefficients includes a first set of filter coefficients associated withthe filter and forming a first portion of the set of filter coefficientsand a second set of filter coefficients associated with the filter andforming a second portion of the set of filter coefficients; means forgenerating the first set of filter coefficients using predictivedecoding based on the first set of residual values and filtercoefficients associated with a fixed interpolation filter; means forgenerating the second set of filter coefficients using predictivedecoding based on the additional residual values and the first set offilter coefficients, wherein the additional residual values are relativeto one or more mirrored or rotated values of the first set of filtercoefficients; and means for applying the set of filter coefficients tointerpolate predictive data used for predictive decoding of videoblocks.
 27. The device of claim 26, further comprising means for entropydecoding the received residual values and the received additionalresidual values and means for de-quantizing the received residual valuesand the received additional residual values prior to generating the setof filter coefficients and applying the set of filter coefficients. 28.A non-transitory computer-readable storage medium comprisinginstructions that when executed by a processor cause the processor to:identify a set of filter coefficients associated with a filter forinterpolation of predictive data in video encoding, wherein the set offilter coefficients comprises a first set of filter coefficientsassociated with the filter and forming a first portion of the set offilter coefficients and a second set of filter coefficients associatedwith the filter and forming a second portion of the set of filtercoefficients; generate residual values associated with the first set offilter coefficients based on predictive coding of the first set offilter coefficients relative to filter coefficients associated with afixed interpolation filter; generate additional residual valuesassociated with the second set of filter coefficients relative to one ormore mirrored or rotated values of the first set of filter coefficients;apply quantization to the residual values and the additional residualvalues; and entropy code and output the quantized residual values andthe quantized additional residual values as part of an encodedbitstream.
 29. The non-transitory computer-readable storage medium ofclaim 28, wherein in identifying the set of filter coefficients, theinstructions cause the processor to perform an adaptive interpolationfiltering process to determine the set of filter coefficients andgenerate the predictive data.
 30. The non-transitory computer-readablestorage medium of claim 28, wherein at least some of the residual valuesassociated with different filter coefficients are assigned differentlevels of quantization.
 31. The non-transitory computer-readable storagemedium of claim 30, wherein at least some of the residual values withsmaller magnitude are assigned finer quantization and at least some ofthe residual values with larger magnitude are assigned coarserquantization.
 32. The non-transitory computer-readable storage medium ofclaim 30, further comprising instructions that upon execution cause theprocessor to entropy code the quantized residual values and thequantized additional residual values prior to outputting the quantizedresidual values and the quantized additional residual values, wherein inentropy coding the quantized residual values and the quantizedadditional residual values the instructions cause the processor toassign a prefix code, assign a suffix code and assign a sign value forthe quantized residual values and the quantized additional residualvalues following quantization of the residual values and the additionalresidual values.
 33. A non-transitory computer-readable storage mediumcomprising instructions that when executed by a processor cause theprocessor to: upon receiving residual values and additional residualvalues associated with a set of filter coefficients associated with afilter, wherein the set of filter coefficients includes a first set offilter coefficients associated with the filter and forming a firstportion of the set of filter coefficients and a second set of filtercoefficients associated with the filter and forming a second portion ofthe set of filter coefficients, generate the first set of filtercoefficients using predictive decoding based on the first set ofresidual values and filter coefficients associated with a fixedinterpolation filter; generate the second set of filter coefficientsusing predictive decoding based on the additional residual values andthe first set of filter coefficients, wherein the additional residualvalues are relative to one or more mirrored or rotated values of thefirst set of filter coefficients; and apply the set of filtercoefficients to interpolate predictive data used for predictive decodingof video blocks.
 34. The non-transitory computer-readable storage mediumof claim 33, further comprising instructions that upon execution causethe processor to entropy decode and de-quantize the received residualvalues and additional residual values prior to generating the first andsecond sets of filter coefficients and applying the set of filtercoefficients.